Open-World Drone Active Tracking with Goal-Centered Rewards
Authors: Haowei Sun
Jinwu Hu
Zhirui Zhang
Haoyuan Tian
Xinze Xie
Yufeng Wang
Xiaohua Xie
Yun Lin
Zhuliang Yu
Mingkui Tan
Venue: NeurIPS 2025 poster ()
Keywords: Drone Tracking Agent; Drone Active Tracking; Goal-Centered Reward Design; Open-World Tracking Benchmark; Cross-Domain Generalization in Drone Tracking
Primary area: reinforcement_learning

Abstract: Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations, providing a more practical solution for effective tracking in dynamic environments. However, accurate Drone Visual Active Tracking using reinforcement learning remains challenging due to the absence of a unified benchmark and the complexity of open-world environments with frequent interference. To address these issues, we pioneer a systematic solution. First, we propose DAT, the first open-world drone active air-to-ground tracking benchmark. It encompasses 24 city-scale scenes, featuring targets with human-like behaviors and high-fidelity dynamics simulation. DAT also provides a digital twin tool for unlimited scene generation. Additionally, we propose a novel reinforcement learning method called GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios. Specifically, we design a Goal-Centered Reward to provide precise feedback across viewpoints to the agent, enabling it to expand perception and movement range through unrestricted perspectives. Inspired by curriculum learning, we introduce a Curriculum-Based Training strategy that progressively enhances the tracking performance in complex environments. Besides, experiments on simulator and real-world images demonstrate the superior performance of GC-VAT, achieving a Tracking Success Rate of approximately 72% on the simulator. The benchmark and code are available at https://github.com/SHWplus/DAT_Benchmark.

Open-World Drone Active Tracking with Goal-Centered Rewards
Haowei Sun Jinwu Hu Zhirui Zhang'? Haoyuan Tian Xinze Xie Yufeng Wang Xiaohua Xie Yun Lin" Zhuliang Yu! Mingkui Tan
South China University of Technology, IIstitutee for Super Robotics (Huangpu). Pazhou Laboratory, Key Laboratory of Big Data and Intelligent Robot, Ministry of Education, Peng Cheng Laboratory, Sun Yat-sen University, Harbin Engineering University
Abstract
Drone Visual Active Tracking aims to autonomously follow target object by con- trolling the motion system based on visual observations, providing a more practical solution for effective tracking in dynamic environments. However, accurate Drone Visual Active Tracking using reinforcement learning remains challenging due to the absence of a unified benchmark and the complexity of open-world wooldd environ- ments with frequent interference. To address these issues, we pioneer a systematic solution. First, we propose DAT, the first open-world drone active uir-to-ground tracking benchmark. It encompasses 24 city-scale scenes, featuring targets with human-like behaviors and high-fidelity dynamics simulation. DAT also provides a digital twin tool for unlimited scene generation. Additionally, we propose a novel reinforcement learning method called GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios. Specifically, we de- sign a Goal-Centeree Reward to provide precise feedback across viewpoints to the agent, enabling to expand perception and movement range through unrestricted perspectives. Inspired by curriculum learning. we introduce a Curriculum- -aaneddsed Training strategy that progressively enhances the tracking performance in complex environments. Besides, experiments on simulator and real-world images demon- strate the superior performance of GC-VAT, achieving Tracking Success Rate of approximately 72% on the simulator. The benchmark and code are available at htttps://gitaii thtbbbgtttttttt//// wow/SHWplll DAT Benchmark
Introduction
Visual Active Tracking (VAT) aims autonomously follow a target object by controlling the motion system of the tracker based visual observations [80 75 is widely used in real-world applications such as drone target tracking and security surveillance 22
54 Unlike passive visual tracking 3,,74 33 5 9 84 67 (88,, which involves proposing 2D bounding box for the target on frame- by-frame with fixed camera pose, VAT actively adjusts the camera position to maintain the target within the field of view. Passive visual tracking often falls short in real-world scenarios due to the highly dynamic nature of most targets. Thus, VAT offers a more practical yet challenging solution for effective tracking in dynamic environments.
Recently, VAT methods have evolved into two main categories: pipeline VAT methods and reinforcement learning-base -bsed VAT methods [19
40
80] Pipeline VAT methods employ 33 and control models are connected in series. The tracking model estimates the target position in the input image and the control model
39
18
sequential framework where the visual tracking 32
"Equal contribution. Email: sunhoward 105@ 105@@00il..oommo com, fhujinwu @gaalllmllmoommmmmmmoooooo I "oorespondiigg author. Email: mingkuitan @ww...ddu.cn zlyu scut.edu.cn
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Table I: Comparison of DAT benchmark with simula- tors where existing methods are located.
Gual-Centered Reward
V D-V 19]
AOT 39
DAT
GC-VAT
Ageut
00=
E
Li DAT
temairaggh
Scenes
24
Actions
Targets
24
Update State
Inllipp pa chanpy
Tracker
Dynamics
Ground
Simplified
Drone
Ground
X
Full Physies
Both
image
real world
estnwrape)
like target behaviers
Target Behavior
Policy-based
Rule-based Rule-based
Human-like
Scene Building
Manual
Manual
Manual
Digital Twin
Figure I: pipeline for drone VAT.
generates control signals. While this modular design allows for clear task separation, it often requires significant manual effort to label the training data, and the control module requires additional tuning for different scenes. To address these issues, reinforcement learning-based VAT methods integrate visual tracking and control within a unified framework. These methods eliminate the need for separate tuning of the tracking and control modules by using unified framework map raw visual inputs directly to control actions. Therefore, the reinforcement learning-basse VAT methods simplify system design and increase the efficiency of learning adaptive tracking behaviors in dynamic environments.
Unfortunately, achieving accurate drone VAT with reinforcement learning remains challenging, partly for the following reasons. 1) Missing unified benchmark. Existing benchmark scenes are low in complexity, neglect tracker dynamics or rely on overly simplified models, making them inadequate to use rule-based target management, far from producing human-like target behaviors. Additionally, current 3D scenes are all manually constructed, leading heavy workload and limited scene number. 2) Vast environments with complex interference. Open-world tracking involves large, dynamic environments with frequent interference. In previous methods (39 19,, trackers can only capture images from fixed horizontal viewpoint. However, the fixed forward viewpoint captures excessive sky, reducing target-related visual information, especially for sur-to-ground tracking tasks. Besides, since VAT goal to keep the target at the image center, such viewpoint restricts the tracker to the same height as the target, severely limiting the perception and movement range. Moreover, training directly in complex conditions leads to slow convergence or difficulty in building strong behaviors.
validate the agent performance (see Table T Previous methods [39 19] 57)
To address the above limitations, first propose DAT, the first open- world active air-to- to-gruund tracking benchmark that simulates real-world complexity (see Fig. [b)). Specifically, DAT provides 24 city-scale scenes, full-fidelity simulations of drone dynamies, and a lightweight tool that can be integrated into any 3D scene to enable human-like target behaviors. It also offers a digital twin tool that can generate unlimited 3D scenes from real-world environments, enabling unlimited scene expansion. Second, we propose a novel drone VAT with reinforcement learning method (called GC-VAT), aiming to improve adaptability in complex and diverse scenarios. Specifically, a Goal-Centered Reward to provide precise feedback across viewpoints, enabling the agent to expand perception range through unrestricted perspectives. Besides, we propose qualitative and theoretical methods to analyze the effectiveness of our reward. In addition, inspired by curriculum learning Training strategy that progressively improves agent
design
665 [T 833, we propose a Curriculum-BBas performance in complex environments. Our contributions are summarized as follows:
A comprehensive drone active tracking benchmark. We present DAT benchmark, featuring high-fidelity dynamics, 24 city-scale scenes, and tools for simulating human-like target behaviors and unlimited scenes generation, enabling rigorous algorithm validation. 2) A novel drone active tracking method. We propose GC-VAT, which leverages a Goal-Centered Reward function and a Curriculum- -Based Training strategy to enhance drone tracking performance in complex and dynamic environments. Besides, we propose qualitative and theoretical methods to analyze the effectiveness of our reward. 3) Extensive experimental validation. Experiments on simulator and real-world images validate DAT usability and GC- VAT effectiveness, with VAT achieving a Tracking Success Rate of approximately 72% on the simulator.
2 Task Definition of Drone Active Tracking
DAT task seeks to train model to control a drone for active target tracking in dynamic environments (see Fig. DT. Using visual and motion sensor data, the model learns actions keep the target centered in view, ensuring robust performance across diverse scenarios.

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DAT Challenges
11
Scene Areatkm
Caler Richness
3845
68L1
SHaTON
10
36.7
STAI
24.
Building (enslty(km-))
Dendiy(kan
Read Deasitytkae")
Example scenes
50
Loged
Village
Dowatown
Bann uraght quad bulk lifh
Farmland
Terrain Dessitylumr)
Taand (easits(kaa))
Devert
Statistics of challenges in DAT scenes (including richness, the density of buildings, trees, roads, mountains, and tunnels). (c) Human-tike like behaviors of targets.
color
Gafle Jan
(d) Target examples in DAT.
Figure 2: Statistics and simulator component examples of DAT. (a) Statistics on 7 complexity aspects in DAT scenes. (b) Example scenes of DAT. (c) Diverse behaviors of targets. (d) Examples of the tracking targets. More details can be found at battts::/githhi com/ HHllWlus/DAT DOAT_Becchaar,
Observation spaces. The target is initially positioned at the center of the field of view, and the observation space comprises data acquired from sensors (e.g., RGB images with 84 84 resolution).
Action spaces. The action space can be either discrete or continuous. A discrete space defines a set of predefined drone maneuvers, whereas a continuous space allows direct control over the velocity.
Success criterion of DAT task. We define a success criterion when the model can keep the target object, which is initially located at the center of view, in the middle of the image for long duration.
Challenges. Open-wordd world drone active tracking is challenging due to limited data and high risks of trial-and -ar--rrrroor in the real world, necessitating complex simulation environments. Additionally, the complexity and dynamics of open-worke world scenes further demand robust agent performance.
3 DAT Benchmark with Diverse Settings
We develop DAT, including 24 city-scale scenes built by an unlimited scene generation tool, high- fidelity drone dynamics simulation, and versatile pipeline for producing human-lin target behaviors.
3.1 Diverse Scene Construction
Digital twin tool. Users can select any region from OpenStreetMap [ to obtain countless scenes using tool. Specifically, it generates high-precision road network with traffic lights and rules, and it converts elevation and vegetation data into 3D assets placed in the scene. Moreover, all assets in the generated scene editable, allowing for data augmentation. See Appendix for details.
Scene construction. Based on the above tool, we construct outdoor scenes under 4 weather conditions, modeling real-world complexities. Specifically, the scene area, building density, and color richness depict the complexity of the visual background. Road density and terrain density affect the target behaviors. The tree density and tunnel density measure the level of visual occlusion. As shown in Fig. 2 a), six scenarios exhibit unique and realistic complexity across the seven aspects:
Citystreet scene covers an area of 0.7 square kilometers. It has road density of 38.7 and a tree density of 97.5, mainly testing the agent's efficiency against tree occlusions.
Village scene spans 1.4 square kilometers. This scene features a mountain density of 20.1 and a tunnel density of 6.3, requiring the agent to predict the target's movement when it is fully obscured.
Downtown seene covers 0.8 square kilometers. includes complex road elements and high building density of 304.9, challenging the agent's 's tracking accuracy and obstacle avoidance abilities.
3

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Lake scene encompasses 1.6 square kilometers. The density of road elements is 68.1, 1, and the richness of background colors is 5, challenging the robustness across varying features and colors.
Farmland scene covers an area of 0.7 square kilometers. The color richness is 5 and multiple color patches, challenging the agent's adaptability multi-color environments.
Desert scene covers 1.1 square kilometers. includes a mountain density of 37.1 and a road density of 31.0, Some roads are covered by sand, testing the agent's adaptabilityy to such conditions.
Four weather conditions are designed test the agent's 's cross-domain adaptability. Foggy reduces visibility, night reduces brightness, and snow alters the color. The above 24 scenes (see Fig. 2b) can fully measure the agent tracking performance. See Appendix B for scene construction details.
3.2 Various Trackers and Targets Construction
Drone Active Tracking in the real world involves diverse targets depending on tasks. DAT provides diverse targets with human-like behaviors and enables high-fidelity tracker dynamics simulation.
Tracker. DAT benchmark supports two tracker types: drones and ground robots. The drone used is the DJI Matrice 100 [14), equipped with a 3-axis gimbal, allowing for precise camera adjustments.
Unlike simpler kinematic models in [19] and methods that ignore the dynamics 39. DAT' leverages webots 37 to simulate the drone's s full dynamics, including mass, inertia, aerodynamics, and the response and jitter of the gimbal, closely matching real drones. See Appendix B for details.
Targets. DAT includes five categories of targets: automobile, motorbike, pedestrian, wheel robot, and legged robot, with a total of 24 tracking targets (see Fig. dd). See Appendix B for details.
Target Management. We propose a novel pipeline to simulate realistic target behavior. Specifically, DAT first utilizes road networks generated by the tools described in Section and directly integrates them with the SUMO traffic simulator [36]. Then, random trajectories are assigned to each vehicle, with SUMO managing its motion. To bridge the gap between simulation and visualization, implement a controller that translates motion data into human-like driving behaviors for 3D vehicles (see Fig. (c). Our controller also adheres traffic rules and can simulate phenomena such as traffic light waits and traffic jams. Even better, the controller can be applied to any 3D scene.
4 VAT with Reinforcement Learning
In this paper, we primarily focus on visual active tracking (VAT), core task within DAT benchmark. We propose a drone visual active tracking with reinforcement learning method called Goal-Centered- VAT (GC-VAT), aiming to improve the performance of tracking targets in complex scenes. As shown in Fig. D we model drone active tracking Markov Decision Process (MDP) and train Drone Agent capable of adapting to unrestricted viewpoint conditions to track target in the open scene.
4.1 MDP for Drone Active Tracking
We seek to learn end-to-end drone tracking policies in dynamic environments by modeling the task as an MDP: (8, A, R, y, T). In this representation, S denotes the state space, A represents the action space, and is the discount factor. At each time step 4, the agent takes the state E S as input and performs action de E A. Next, the simulator transitions to the next state 8++1 T(s,, ())) and calculates the reward r. = R((t, az) for the current step. The details of the MDP are as follows:
State S is the visual information of the scene. At each time step t, the camera captures one image of size 84 84 as the current state.
Action A is a set of discrete actions, including forward, backward, leftward, rightward, turn left, turn right, and stop movements. At each time step, the Drone Agent selects an action at based on the state and actively controls the camera movement.
Transition T(st; ar) is function T S A S that maps 8, 8141. In this paper, the webots dynamics engine to provide a realistic transition function.
Reward R(s,, ax) is the reward function. The goal-centered rewards are given in Section 4.2

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R02
10
LD
RD
RD
LD
4.00
4/. 8.
Ryg
17m
10p
RV, (b) Deviation and contours (Ours)
LUG
Camera projection and pinhole imaging (b) Reward acquisition
Reward contours of D-VAT
Figure 3: Diagram of reward acquisition.
Figure 4: Reward design analysis diagram.
Network structure of Drone Agent. Similar to previous works
39 80
we select a backbone
architecture consisting of CNN followed by a GRU network
(see Appendix C.2).
Key Challenges in Drone Active Tracking. In open-world environments, drones face unpredictable target behaviors and frequent occlusions. Designing a single reward that encourages diverse and robust tracking actions is extremely difficult. To address this, propose goal-centered reward in Moreover, given the vast observation space, discovering successful policies is non-trivial. To facilitate efficient learning, introduce curriculum-based training strategy in Section 4.4
Section 4.2
4.2
Goal-Centered Reward Design
For drone tracking ground-based target, our reward is designed to characterize the target's position in the image and guide the drone to keep it centered. Therefore, we first need to select an appropriate distance metric to quantify the proximity between the target and the center in the image plane.
Since the drone typically captures images from a top-down perspective, the image plane is not parallel the ground. Due to the affine transformation, the projection of the image plane becomes trapezoid (see Fig. ()). and the physical distance between the drone and the target cannot directly correspond their pixel distance. Existing methods [39] [9] compute the Euclidean distance between the drone and the target, which may not accurately reflect their spatial relationship in the image plane.
To address this issue, employ deviation metric d( to measure the distance between the target and the image center projection, as illustrated in Fig. ()). Specifically, given a target point Pa and the image center projection Cg. the deviation metric computed by
P. P
( C2) (P C2) C
(1)
where 1 Cyl denotes the distance from Pg to Cg and E (Pg Cg) represents distance from point (p( Cg) to the center. Eg (Pg. Cg) is the intersection the line connecting Pg and Cy with the projected image boundary, shown in Fig. (b).
The deviation .)) designed to ensure that targets inside the image closer to the center than those outside, with contours shown in Fig. 4b).
Principles for Reward Design. The objective of the VAT task is to keep the target at the image center. Thus, the targets closer to the image center projection should get higher reward values. For deviation metric ((.)),, -), the design principle of the reward function (.)()) is defined as:
YP1, P2 E W, if Φ1 < Φ2, then (Ø)))) > (
(2)
where W denotes the valid region with non-zero reward, and Φ1, 62 represents the deviation from the target point to the image center projection.
Goal-Centered Reward Function. Our reward (-)(-) decreases as the target moves away from the projected image center Cy. and is zero if outside, as shown follows:
tanh (a(1-o(P,, Cg))", Pg E Iali
rge( (Pg) (Py)=
(3)
0, otherwise
The attenuation degree of (-) can be adjusted using the hyperparameter set to 4. The tanh(-) provides a strong indication the task goal due to its relatively quick decay at the image center. Letip is the truncated image range set to prevent the drone from keeping the target at the edge of the image. The truncation of the image can controlled using the hyperparameter Aetip as: Actip = HI H, where H and Hi the heights of the original and the truncated image. We set Xelip 0.7.
5

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Algorithm Curriculum-BBan Training (CBT)
1: Input: Initial policy parameters Oo. phase threshold total steps N, rollout steps
2: Initialize: Training phase phase <- reward buffer B 0, rollout buffer B, t
3: for each step k = ...,, N do
4: if phase 1 then
5:
Configure simple environment: linear target trajectories + no obstacles
6:
else
7:
Configure complex environment: varied target movements obstacles/occlusions
8:
Collect transition Tk (st, 1141, at TT) with rewards calculated via 33)
9:
Append to buffer: B + B rk. B, +- Br 7k if mod n 0 then
10:
11:
Update policy using PPO: 0x+1 + PPO_ OPO__Udatee{Ap B3))
Clear rollout buffer: B,
if phase and [8] Er.er 2 then
Switch training phase: phase <
15:
Clear buffer: B + 0. B 0
More details about the Goal-Centered Reward. The reward function (Eq. relies the projections of the four corners and image center to compute deviation o(.,) :) As shown in Fig. 3 a). in the camera frame {c}, the image center and four corner points have the coordinates C(-f, 0, 0).
LU H). LD(-f- _ W. Hf). (-f W, _ H). (D(-f W - Hf), where W and H are the image width and height and f denotes the camera focal length, which can be computed using W
the pinhole imaging principle [8] as: f can ( FaV) FoV is the camera field of view. Next, the
equations of the lines connecting the image center and the four corner points the optical center (O, O, 0) can be obtained in frame (light blue dashed lines in Fig. 3(a)::
Iavo. = WW
2y I.no. - = = -
IRUO. - 2y 2:
(4)
H
IRDO, : - W -H
2y 2:
Ico. : = 0,
where Iuvo, is the line connecting LU to O., similarly for ILDO LUUO, IRDO, and lco.- Thus, the projections of the points be obtained by intersecting the lines with the ground plane.
Therefore, we next derive the expressions for the ground plane and the target. For clarity, adopt unified representation in frame {c}. In DAT scenes, the road surfaces are smooth. Thus, in the world frame {w) (see Fig. 3(a), the ground plane G defined as: = h, where denotes the ground height. For simplicity, we here express G., in {c} as Ge Agx+ Byy + Dy = O, with Ag. By. Cg. D, derived in Appendix C.2 Furthermore, the target coordinates P. = (Iv, 20, 1)T in {w} can be transformed to {c} using homogeneous transformation matrix 77 Tow: Pg = T-1 P...
Subsequently, the ground projections can be obtained by intersecting lines in Eq. 4 and G.: LU, (-J: (-W:-WW (T)uu
D2(A,f 1 B,W H)
:-f. -.W.W.1H HHtia
tua Dy(A,f B,W CC
: (-f. $ H
fru Dy(Agf B,, -)
(5)
RD, (-f, 1,, -.W.-11 H))mm Da 2, 0000)
t.d D,(A, - B,B,W + -,
C
where LUg, LDg, RUg, RD, and C, are the projections of LU, LD. RU, RD and C. Using target
coordinates P3 and Eq. [ the reward is computed Eq. 3 See Appendix C.2 for details.
4.3 Theoretical Guarantees Reward Design
Existing methods 33 19 assume fixed forward camera view and use distance-bass rewards. However, when the view changes, these rewards may fail due to the affine transformation effect in

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image projection. We hereby provide a theoretical analysis to show that commonly used distance- based rewards will fail when the camera deviates from a fixed horizontal forward view.
To this end, we define Ra(-)) as a distance-based reward using Euclidean distance between the target and the image center projection. A distance-base based reward Ra(()) satisfying Eq. 2 may still assign higher rewards to targets farther from the center under the metric o(-, -), rendering it ineffective. In contrast, any deviation-basee reward R.(-) satisfying Eq. can effectively reflect the target position.
The commonly used Euclidean distance d(.;,) between the target and the image center proposition does not align with the deviation o(:-)) of the target from the image center projection, when the camera is not at a fixed horizontal forward viewpoint. That is:
Proposition 1
3P, Pa E Ip, s.t. &1 <02, ((P,,C))d((2.CC))
(6)
where o; = &(P., Cg), P, are points in the projection region Ip Cg is the image center projection. See Appendix for theoretical proof.
Remark 1. A distance-based reward (.).) satisfying Eq. results in targets closer to the center receiving lower rewards, when the camera is not fixed horizontal forward viewpoint. That is:
P.Ep, s.t. Φr 62. R.((s)) < Ra(d2),
where d, = d(P;, Cg), and o; = o(P. (p) This illustrates the failure of the distance-based reward under these viewpoints. See Appendix C. for theoretical proof.
Qualitative Analysis. According to the Theoretical Analysis above, rewards should decrease monotonically along the deviation contours in Fig. 4b) the target moves toward the projection boundary. Thus, the reward contours must align with the deviation contours. The contours of r ge)(+) in Fig. 4 4b) perfectly align, indicating accurate position feedback. In contrast, D-VAT [19 (see Fig.
4 aa) shows misaligned contours, explaining its failure as noted in Remark 1.
4.4 Training with Curriculum Learning
DAT scenes contain numerous dynamic targets and obstacles, hindering convergence and performance. Progressively training the agent from simpler to more complex environments enhances performance and accelerates learning for the final task (63] Therefore, we propose Curriculum-Baan Training (CBT) strategy to optimize reinforcement learning training in complex environments.
To address the challenges, we employ the Proximal Policy Optimization (PPO) [55] algorithm, known for its efficiency in control tasks. To further enhance agent adaptability and robustness, we apply domain randomization during agent training. Specifically, we randomize the drone's initial position and orientation relative to the target to promote diverse behaviors. Additionally, we randomize the gimbal pitch angle to improve the agent's 's spatial perception. See Appendix C.2 for further details.
Given the scene complexity, we adopt CBT strategy, which divides the model training into two stages. The first stage consists of simplified environment with straight line target trajectories and no obstacles. The agent learns to center the target through the reward r; in Eq. In the second stage, the agent encounters more varied target movements and complex visual information, such as tree occlusions and crosswalks. The goal of the agent to develop stronger generalization abilities based on task understanding in the first stage. See Algorithm I for the pseudocode of the CBT strategy.
5 Experiments
5.1 Experimental Settings
Experimental Setup. We conduct cross-scene and cross-domain tests. The former tests an agent trained under daytime conditions in unseen scenes with the same weather. The latter evaluates the agent in the same scene under varying weather conditions. See Appendix E. for details.
Metrics. We cumulative reward (CR Emar rge) and tracking success rate (TSR
Emn EEn Fa x 100%) to evaluate the agent performance. CR primarily reflects how well the agent centers the target over episode length F,, while TSR measures the ability to keep the target in view, with rat = 1 meaning the target is within the view (See Appendix C). and Enz denoting the
7

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Table 2: Results of within and across scenes on DAT benchmark.
Method
cityttret
desert
village
downtown
lake
farmland
CR
TSR
CR
TSR
CR
TSR
CR
TSR
CR
TSR
CR
TSR
Within Scene
AOT
43 0.2510.02 911 00000
4615 0.201 5415 0.2911.01 47 0.242
20 0.23
D-VAT
4828
0.2640.02 02 17 0.26
44
0.22+0.05 9=1 0.06+0.01
46=8 0.26
13
0.07=0 20.00
Ours
11000.00 + 124 0.84
2. 2929
1.1 0. 119 0.65 10 181+1160.61 +0.31
134
243 117
11..61170...... #0.32
Cross Scene
AOT
48+5 0.24
9+0 0.06+0.00
52+11 0.25+
52+6 0.28+0.03 48
0.24
49+7
0.24
D-VAT
4919
0.2611.04 48
0.27 50+14 0.25
821 0.05 51 0.25
14+1 0.07 =0.00
Ours
144 0.522..229 20.
101 00.4 115 20.20 162 4.52
Table 3: Results of cross domain on DAT.
citystret-ddy
desert-day
downtown-day
Nurood
Boward
Method
night CR
TSR
CR
TSR
foggy
TSR
CR
snow TSR
AOT
42a4 0.2220.02 4417
0.22 0z
44 0.22
D-VAT 3547
0.1910.03
37 0.19
3416
0.20 03
Ours
217 0.64 214 0.76 25 178 0.60 26
AOT
MOT
LL5 IM
MtepeMM
au BM
23 As the
Table 4: Results of ablation experiments on DAT.
farmland-day
lake-day
villagedda
Wathin-Scent CR
Crass
Doma-in TSR
Method
TSR
CR
TSR
CR
Roaar
Stord
Ro-var
9.1 0.06 811 0.05 920
0.06.2000
5
w/o CBT
4622
0.23
53_16 0.26 o7 46g2 0.23
w/o AR
1064sa 0.44
92=72 0.
80=63 0.36+
0-00
w/o HR
174+11s0.499
wo VR
231
Stqp(hi)
Stept/t
Soopthi)
w/o 139+119 +0.33 124-as
0.48+a.25
142 +o.2
Figure 5: Reward values during training.
Ours 243 0.68 162= 0,54 222 0.65 20.27
maximum episode length. Agents are initialized at four relative angles to the target (0, 3. 3 rad), with 10 episodes per angle (40 total). The mean and variance of these results are calculated for each map, and the final cross-scene and cross-domain performance
averaged across different scenes.
Baselines. We reproduce two SOTA methods: AOT 39 and D-VAT 19] Both baselines and other methods [8188 use distance-based rewards. As concluded in Section 4.3 they may fail in tilted top-down views. Thus, these baselines sufficiently highlight GC-VAT superiority. See Appendix D
5.2 Comparison Experiments
We compare our GC-VAT with the SOTA methods for within-scene performance and cross-scene cross-domain generalization performance on DAT benchmark. As shown in Fig. [ our method achieves consistently higher and steadily increasing rewards throughout training, demonstrating its effectiveness. Both AOT and D-VAT methods fail to learn effective policies due to the misleading feedback from their distance- of view, resulting in a rapidly declining reward curve. The results validate the theoretical analysis in Section (4.3] It worth noting that although AOT and D- D-V exhibit low variance in their experimental results, consistently low rewards typically indicate failure to learn effective tracking policies.
rewards. In particular, AOT learns to quickly drive the target out
Within-scene performance. We train the model on all scenes and evaluate it on the original scene. Our GC-VAT performs significantly better than other methods as shown in Table 2 For the CR, the average performance improvement on six maps relative to the D-VAT method is 591%(35- -3 Regarding the TSR, the average enhancement is
Cross-scene performance. Our method demonstrates strong cross-scene generalization, as shown in Table 2 Specifically, GC-VAT achieves a 377% + 176) improvement in average CRR and 200%(0.15 + 0.57) improvement in average TSR compared to D-VAT
performance. As shown in Table 3 our method outperforms existing methods significantly in cross-domain generalization. Specifically, GC-VAT demonstrates an average CR enhancement of 509%(35 (35 +213) relative to D-VAT and TSR boost of .0.6
8

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Table 5: Performance under wind disturbances and target distractors.
Table Performance under rainy conditions and unseen targets. We evaluate the model trained on citystreet-day.
CR
TSR
Wiehin-Scene
Crows-Scene
Cress-Domals
w/ Forward
302:194
0.91 1:018
Method
CR
TSR
CR
TSR
CR
TSR
w/ Lateral
304:882
1.
w/ Yaw
3014:12
0.88
rain
260
0.
139 109
0.45 so 274 2.
w/ Distractor
293:::20
0.91 10.15
Unseen Target
222_92
0.79
131 0.50
207 0.79
Ours
316:88
2.
Ours
110 20.30
144 0.52 1 0.82
Target Center Position (py).
2000
150
Forward
Barksard
Table Effectiveness of GC-VAT on Sim2Real test. We select eight video se- quences from each dataset for evaluation.
Risht
Video
VOT 30
DTB70
35
UAVD7 20
-50
-100
Average Correct Action Rate
Random
0.413
0.426
0.421
-200
Target Center Position (px)
-1.0
Ours
0.795
0.833
0.802
Figure 6: Results on real-world images.
5.3 Ablation Experiments
We conduct ablation experiments on Goal-Centered Reward to validate the results of the analysis presented in Section 4.3 Moreover, we verify whether the Curriculum-Baa Based Training strategy and domain rondomization from Section 4.4 lead to significant performance improvement. We present
results on the farmland map Table with additional results provided in Appendix E.3
Effectiveness of reward design. We contrast the performance of GC-VAT method when using the reward defined in Eq. and that in [19]. As shown in Table significant performance enhancements (about 800% improvement in 75R across-scene and cross-domain) are evident with the utilization of Eq. 3 These results strongly corroborate the analysis in Section 4.3 and underscore the effectiveness of the proposed reward. See Appendix E.3 for more experimental results.
Effectiveness of CBT strategy and domain randomization. As shown in Table 4 without the CBT strategy, the model fails to learn effective tracking policies, resulting in consistently low rewards across different tests. In addition, improvements. Specifically, AR, HR, VR, and PR denote the randomization of the drone's 's initial angle, horizontal and vertical distance relative to the target, and gimbal pitch angle, respectively. Among these, A R contributes the most to performance gains, indicating that encouraging diverse actions through angle randomization facilitates the agent's exploration of optimal policies.
domain randomization approach yields significant
Robustness under wind gusts and precipitation. To further investigate the impact of real-world dis- turbances on the GC-VAT method, conduct rigorous tests under wind gusts and sensor degradation caused by precipitation. Specifically, we simulate wind effects by applying randomized perturbations along the forward, lateral, and yaw directions during testing. The results are summarized in Table [5] where the model trained on citystreet-day and evaluated on sitystreet-fogyy with added wind perturbations. The Tracking Success Rate (TSR) drops by less than 0.06, demonstrating that GC-VAT maintains strong robustness under significant wind disturbances. See Appendix E.3 for more details.
To simulate the blurring caused by raindrops, we follow established practices in test-time adaptation literature (34). Specifically, we train the policy on citystreet-day map and evaluate under synthetically generated rain in within-scene, cross-scene, and cross-domain settings. To ensure realism, we exclude snowy conditions from the cross -oosann evaluation, as snow and rain rarely co-occur in real-world environments. The results in Table 6 show only marginal performance degradation (less than 0.07 in TSR) under rain simulation, confirming that GC-VAT is robust to blurring caused by raindrops.
Robustness to distractors and novel targets. As shown in Table our model maintains high tracking performance even when a similar-look looking vehicle is introduced near the target, demonstrating its ability to effectively distinguish the true target from confusers. In addition, we evaluate GC-VAT on an unseen target class (bus). As shown in Table our model maintains strong tracking performance, with TSR drop of less than 0.03 when encountering this novel object.

---

Laptop
Phone
Controller
TCP
USB
UDP
(a) Forward
(b) Rightward
Algorithm
Image Receiver
Mobile
1166
Decnder
Android System
Predieted Actions
GC-VAT
ccw
DJI Mobile SDK
(c) Turn right
(d) Leftward
Figure 7: Schematic of the real-world deployment pipeline.
Figure 8: Results on real drones.
5.4 Experiments in Real-world Scenarios
Effectiveness real-worid images. Due to the difficulty real-robot evaluation, we follow 39 and validate GC- VAT on real images. We perform zero-shot transfer tests using 8 videos each from VOT 30]. DTB70 B5 and UAVDT [20] datasets. Although camera control is unavailable in recorded videos, we can feed frames into the model and verify the reasonableness
its predicted actions.
The output actions for the VOT video car16 are shown in Fig. 6 Each point represents the target position the image, with colors indicating different actions. As Fig. [6 (illlustratss, when the target is located on the left (right) side, the tracker tends to move left (right), attempting bring the target to the center. Quantitatively, we use the Correct Action Rate, i.c., the accuracy of predicted actions, to evaluate the performance. As shown in Table 7 VC- achieves an average Correct Action Rate (CAR) of 81.0% across 24 videos, demonstrating its effectiveness. More importantly, is significantly superior to random policy (p 0.001) as verified by t-test. See Appendix E.4 for more results.
Effectiveness on real drones. Furthermore, as a critical step beyond image- evaluation, we conduct real-world experiments on DJI Mini 3 Pro [[3] drone. As shown in Fig. 7 we deploy GC-VAT on a laptop equipped with an RTX 3050 GPU and Intel CPU, use the DJI Mobile SDK [12 to obtain images, and control the drone with the predicted actions. The entire pipeline operates at over FPS. As Fig- iillusttas the model can output actions that maintain the target at the image center. Quantitatively, GC-VAT achieves average zero-shot TSR of 88.4% and CAR of 81.3%. This successful zero-shot Lim-to-Real transfer validates the practical applicability of our approach.
Conclusion and Potential Impacts
In this paper, we propose the first open-world drone active air-to- ---ouud tracking benchmark, called DAT. DAT benchmark encompasses 24 city-scale scenes, featuring targets with human-like behaviors and figh-fidelity dynamics simulation. DAT also provides digital twin tool for unlimited scene generation. DAT benchmark has the potential to impact several key areas, including: 1) Forgetting in Reinforcement Learning, 2) Robustness in Reinforcement Learning. 3) Multi-Agent Reinforcement Learning, and 4) Sim-to-Real Deployment. Additionally, we propose a reinforcement learning- based drone tracking method called GC-VAT, aiming to improve the performance of drone tracking targets in complex scenarios. Specifically, we design a Goal-Centered Reward to provide precise feedback across viewpoints the agent, enabling expand perception range through unrestricted perspectives. Then we propose qualitative and theoretical methods to analyze the reward effectiveness. Moreover, inspired by curriculum learning, we implement Curriculum -Bsed Training strategy that progressively improves agent performance in increasingly complex scenarios. Experiments on the simulator and real-world images validate the analysis and demonstrate that our method is significantly superior to the SOTA methods.
Acknowledgements
This work was partially supported by the Joint Funds of the National Natural Science Foundation of China (Grant No. 024A20377..
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Supplementary Materials for " "Open-World Drone Active Tracking with Goal-Centered Rewards"
Contents
Related Work
16
A.I
Passive Object Tracking
16
Visual Active Tracking
17
Reinforcement learning in Visual Tracking
17
Curriculum Learning in Robot Control
17
More Details of DAT Benchmark
18
More Details of Proposed GC-VAT
21
Theoretical Proof of Reward Design
21
More Details
22
D Baselines
25
E More Experiments
27
Experiment Settings
27
2 Comparison Experiments
27
Ablation Experiments]
28
4
Experiments in Real-world Scenarios
29
Liiitation
29
Related Work
A.1
Passive Object Tracking
Most of the proposed visual tracking benchmarks belong to passive visual tracking. LaSOT 23 and OTB2015 [71] benchmarks contain a large number of ground videos. These benchmarks include target videos, and the tracking algorithms utilize both the video frames and the target labels for tracking. However, ground cameras tend to affected by occlusion and suffer from the shortcoming of limited perceptual range, so the need for drone viewpoint tracking is gradually increasing in practical applications. UAV123 43 and VisDrone2019 21 benchmarks are proposed for drone viewpoint, expanding the spatial dimension of perception. Meanwhile, the single-object tracking benchmarks have difficulties for many targets. MOT20 [17] and TAO [6 benchmarks are proposed for multi-object tracking to solve the above problems. In addition, the above benchmarks include videos from the RGB camera. The RGB camera's 's recognition capabilities are limited in complex scenes, such ocean environments, and challenging weather conditions, including nighttime and foggy. IPATCH [47] provides extra infrared images and other sensors like GPS to supplement the information of the sea scene. Huang et al. propose Anti-UAV410 [88] which provides infrared camera images for drone tracking.
Visual object tracking methods can be categorized into three main types: Tracking by Detection, Detection and Tracking (D&T), and pure tracking. Tracking by Detection methods 5
69 6 treat
tracking as sequence of independent detection tasks. These methods use object detection algorithms identify the target object each frame, connecting the detections through data association methods 31 70 for continuous tracking. While effective in multi-target tracking, these methods
50 52
16

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may suffer from high computational demands and issues with target occlusion. D&T approaches [66]
integrate detection and tracking. creating end-to-end models that ensure seamless information flow and reduce redundant calculations through shared feature extraction networks. Pure tracking methods can categorized into two main types: Correlation Filters (CF) 72 44 27 and Siamese Networks (SN) [32 4 [2]. CF-based models train correlation filters on regions of interest, while SN-based models compare target templates with search areas to enable precise single-target tracking.
79
48
A.2 Visual Active Tracking
Passive visual tracking often falls short in real-world scenarios due to the highly dynamic nature of most targets. Visual Active Tracking (VAT) aims autonomously follow target object by controlling the motion system of the tracker based on visual observations 40 80 775. Thus, VAT offers a more practical yet challenging solution for effective tracking in dynamic environments. Maalouf al. 40 propose a two-stage tracking method (named FAn), which is based on a tracking model and PID control model. This method accomplishes the fusion of perception and decision-making transferring control information from the visual tracking model to the control model. However, the visual network necessitates extensive human labeling effort and the control model requires parameter adjustments for each scene, significantly constraining the model's generalizability. Recently, many XD 19 model the VAT task as a Markov Decision Process and employ end- to-end training with reinforcement learning, resulting in a significant enhancement of the agent's s generalizability.
making by
approaches 19 1
The complexity and diversity of VAT benchmarks are crucial for training agents with high generaliz- ability. One common approach [19
18 80 enhancing environmental diversity involves modifying texture features and lighting conditions within single scene. However, these methods often result in low scene fidelity and unrealistic object placement. While UE4 [24] is used to create photorealistic environments in some benchmarks [80 39], these benchmarks still face limitations in diversity and map size. Furthermore, the scenarios provided by these methods are often task-specific, offering limited configurability and lacking unified benchmark for VAT tasks.
Existing approaches to VAT frequently neglect the randomness of target trajectories and the scalability of platforms. Target trajectories are typically predefined by rule-based patterns [19
S 39]. which significantly restrict the exploration space. Zhong et al. (80) introduce learnable agents as targets, increasing trajectory randomness but adding additional cost. Most benchmarks provide only a
single category of target [19 18 80 39], limiting scalability and necessitating repetitive work for environment development. Zhou et al. (2) utilize CoppeliaSim [2] to provide five categories of noncooperative space objects. However, the use of a solid black background makes it unsuitable for general VAT scenarios. In contrast, our environment supports diverse, real-world target types and offers unified, lightweight management of target behaviors, ensuring both rationality and randomness in their actions.
A.3 Reinforcement learning in Visual Tracking
Reinforcement learning (RL) is widely used in large language models (26) and robot control [53 to improve exploration performance. It is also commonly applied in visual object tracking 776 51 78
Song et al. [59 propose a decision-making mechanism based on hierarchical reinforcement learning (HRL), which achieves ofte-offthheart performance while maintaining balance between accuracy and computational efficiency. However, the actions generated by reinforcement learning in the aforementioned work cannot directly influence the camera's viewpoint, thereby failing to fully leverage the decision-making capabilities. Real-world applications increasingly require robust tracking in highly dynamic scenes, motivating researchers explore reinforcement learning agents for effectively synchronizing visual perception and recision-making in VAT tasks. Dionigi et al. 19 demonstrate the feasibility of reinforeement learning for drone VAT missions. However, the assumption of a fixed-forward perspective limits its applicability in real-world tasks.
A.4 Curriculum Learning in Robot Control
Curriculum Learning (CL.) a training strategy that mimics human curriculum by training models on simpler subsets of data at first and gradually expanding to larger and more difficult subsets of data
17

---

(a)
CitroenCZero
5
Toyota Prius
MercedesBenz Sprinter
RangeRover SportSVR
LinkolnMKZ
Tesla Model3
Bus
Truck
Trailer
Scooter
Motorbike
(b)
(c)
Pedestrian
Shrimp
Create
Sojourner
FireBird6
BB-8
(d)
AiboErs7
BioloidDog
Mantis
Scout
GootDDo
Hoap2
Figure 9: Examples of DAT benchmark targets. (a) Illustration of tracking targets for 10 types of automobile and 2 types of motorbike. (b) Illustration of tracking targets for the pedestrian type. (c) Illustration of tracking targets for 5 types of wheeled robot. (d) Illustration of tracking targets for types of legged robot.
until they are trained on the entire dataset. CL. is widely used in large language models (64 and robot control. As for robot control, reinforcement learning training is difficult due to the complexity of the training scenarios and the large action spaces. Therefore, curriculum learning is often required to reduce the difficulty of agent training. For instance, many works improve the walking ability of
legged robots by adjusting terrain parameters through curriculum learning [53 41 Other studies
improve the pushing and grasping performance of robotic arms by progressively increasing task
difficulty [8 61 45).
In this paper, Curriculum Learning is introduced the VAT task, and the training environment is transitioned from simple features to complex scenarios to achieve successful tracking of agent in complex outdoor environments.
B More Details of DAT Benchmark
More details of the digital twin tool. Our digital twin tool is based on the osm importer tool the webots simulation software. Users first need to download the map description file (.osm file) for specific area from the OpenStreetMap website. Then, the preprocesses the map according to the configuration, modifying information such as the number of lanes and lane directions, and converts the processed file into road network file (.net.mll file) that can read by SUMO. Following this, the tool adds traffic lights and intersection traffic rules the road network based on the configuration,
18

---

Table 8: State parameters of DAT benchmark.
Category
Sensor
Parameter
Type
Description
Potential Tasks
Camera LiDAR
Image
Mat
Images
Default sensor
Vision
LidarCloud
vector2000
Point cloud (m)
Obstacle avoidance
Position
vector3
Position (m)
Visual navigation
GPS
Linear
vector3
Linear velocity (m/s)
Visual navigation
Motion
Accelerometer Gyroscope
Acc Angular ar
vector3
Acceleration (m/s²)
Visual navigation
vector3
Angular velocity (rad/s)
Posture stabilization
Angle Orientation
vectir33
Euler angles (rad)
Posture stabilization
IMU
vector4
Quatermion representation
Posture stabilization
Table 9: Reward parameters of DAT benchmark. The homogeneous transformation matrices (HTM) Tew and Tew are 4x4 square matrices. Therefore, their data type double[16] corresponds a double array of length 16.
Parameter
Type
Description
cameraWidth
double
image width(pw)
camerafeiigh
double
image height(pa))
cameraFov
double
camera field of view(rad)
cameraF Tev
double
estimated camera focal length(pz)
double 16]
HTM of the camera relative to the world frame HTM of the vehicle relative to the world frame ground projection of camera center mapped in the world frame
Tt>
double[16]
cameraMicdGlobalPes
vector3d
carWidGloballoo
vector3d
coordinates of the vehicle center in the world frame coordinates of camera center in the world frame
cameraMidPos
vector3d
carDronePosOri
vector4d
ID orientation + 3D position of vehicle in the drone frame whether tracker collides with building car direction(O-stopp. I-go straight, 2-turn left,3-turn right) tracking target type
crash
double
carDir
double string
carTypename
ensuring that the traffic flow operates correctly when the map is converted into 3D scene. Finally, the tool reads the road, vegetation, and building information and converts them into PROTO assets for webots, which then be correctly recognized and used by the DAT benchmark.
Scenario Construction. Among the DAT scenes, three scenarios: citystreet, downtown, and lake are directly derived from real-world locations with the digital twins tool. Specifically, the citystreet scenario is based on small town in Los Angeles, downtown scenario derived from Manhattan, and the lake scenario is modeled after Wolf Lake Memorial Park in Indiana. In contrast, the village, desert, and farmland maps possess complex and unique features that are not adequately captured by Mpp (OSM) data. For example, the village map features mountainous terrain with tunnels, while the farmland map is characterized by diverse multicolored patterns. To overcome these limitations, we use Creo software [29] to model detailed scene elements, which are then integrated into the webots for constructing realistic maps.
Targets. All tracking target illustrations are presented in Fig. Specifically, Fig. ga) presents automobile and motorbike tracking targets, including passenger vehicles (the first seven cars), buses, trucks, trailers, and motorcycles (such as scooters and motorbikes). These two categories of tracking targets leverage Simulation of Urban Mobility (SUMO) (36) for road behavior modeling and inter- action management with other targets. In contrast, Fig- 9 (d) display pedestrian, wheeled robot, and legged robot tracking targets, respectively. These three types of targets utilize SUMO paths for position initialization and rely specific controllers for action and behavior management.
Sensors. In the real world VAT tasks, a single camera cannot ensure the agent's stability and robustness. Thus, integration with other sensors is often required. The DAT benchmark provides common sensors that can obtain the drone's state parameters relative to the world coordinate system. The drone's position and velocity are determined using GPS, while its acceleration is measured by an accelerometer, providing essential self-referential data for visual navigation tasks. Angular velocity is recorded via gyroscope, and Fuler angles obtained from the IMU are converted into quaternions facilitate state estimation and ensure orientation stability. Additionally, the RPLIDAR A2, provided by DAT, generates point cloud data, which supports tasks such obstacle avoidance and navigation
19

---

A
BOCO
B
A'
B'
Figure 10: Diagram for the theoretical proof of Proposition 1.
by delivering detailed environmental information. The specific sensors, parameters and potential tasks are in Table 8
Additional Parameters. The training process of VAT agents often requires additional parameters for effective reward design. To facilitate this, DAT benchmark provides 4 categories comprising a total of 13 parameters, supporting diverse reward design strategies, as detailed in Table 9
First are the camera parameters, which mainly include image width cameraWidth. image height cameraHeight ght, field of view cameraFov, and focal length cameraF. Utilizing these, the camera plane can be projected onto the ground to aid in reward construction.
Next is the homogeneous transformation matrix (HTM). In the reward design, coordinate transforma- tions are often required to express physical quantities within unified coordinate system, enabling consistent calculations. For example, prior studies [19] 39 [8] transform the position, velocity, and acceleration of targets into the tracker's coordinate system to construct rewards. To support such operations, DAT benchmark provides Teи, the HTM mapping the drone camera coordinate system to the world coordinate system, and Tex, the HTM mapping the tracking target's 's coordinate system to the world coordinate system.
Additionally, for the state of the tracker itself, cameraMidPos represents the position of the drone camera's s optical center in the world coordinate system. The parameter crash indicates whether the drone collides with any buildings the scene, which can be used in reward design for obstacle avoidance tasks.
Lastly, for ease of model training in simulations, reward design often depends on some privileged information, i.c., variables that are almost impossible to obtain in real-world settings. Thus, DAT benchmark also provides such adaptations. For example, BarMidGloball position in the world coordinate system, and carDronePosOri represents the target's orientation and position relative to the drone coordinate system, frequently used in VAT reward design Furthermore, information on the target's 'ss direction and type is provided.
gives the target's
[9
39 18
Task Configuration. We encapsulate the scenes, tasks, and domain randomization into Python classes, and provide different environment classes for different algorithm requirements. The base environment class directly interacts with webots and designed to support asynchronous reinforcement learning algorithms, such as the asynchronous advantage actor-critic (A3C) algorithm
42 The Gymnasium environment class wraps the base environment class into a Gymnasium [60] interface, enabling direct compatibility with popular reinforcement learning libraries, such as Stable- Baselines3
49 and Tianshou [68] for efficient algorithm development and evaluation. The parallel environment class encapsulates the base environment class to enable parallel execution, providing direct support for synchronous algorithms, such as proximal policy optimization (PPO) (55) and soft actor-critic (SAC) (25) Additionally, the scenario selection, tracker and target configuration, SUMO parameters, task additional parameters, and randomization methods can all be efficiently customized through a JSON configuration file.
20

---

11-1
H1
Lanh
Actor: FC7
Hc-1
H4
Tanhh
C8x8-1684
C4x4-3282
Critic:
Figure 11: Network structure of Drone Agent.
citystreet
village
downtown
lake
farmland
desert
Figure 12: Schematic diagram of the training environments for the two-stage of Curriculum Learning
C More Details of Proposed GC-VAT
C.1 Theoretical Proof of Reward Design
Theoretical proof of Proposition 1. Consider points A and B on the image symmetry axis (in Fig. [0), which are symmetric with respect to the image center O. The projections of these points onto the ground are denoted A', B' and O', respectively. Take point C" line segment O' B' such that the Euclidean distance d(O', (O) = d')
Given:
1. In the image plane, the deviation (6:,)) :) of point A and B from the image center O is the same, ie. o(A, O) $(B.O) O).
2. In the projection plane, the Euclidean distance from A' and C" to the ideal position O' are equal, ie. d(A', O') = (()..
is evident that:
1. For any point D' on line segment B'C', the following relationship holds: d(O', d(A', OO)..
2. The corresponding point D in the image lies line segment BC, and thus ((D.) O).
Thus, it is clear that the actual distance between the target and the ideal position inconsistent with the deviation of the target from the image center in the image.
Theoretical proof of Remark 1. According Proposition 1, the following relationship between the Fuclidean distance and the deviation holds:
3P,, Pa E II, s.t. Φ1 < 02, d1 > d2,
(8)
where Φ; ((P. C2) and d, = d(P,, Cg). Therefore, for distance-based reward function R.((-) that satisfies the Reward Design Principle, it follows that:
3P3 Pa E Ipp s.t. di < Φz Ra(d)) Rc(d2))
21

---

Table 10: Total training steps on different scenes. During the training process, we employ a parallel training approach involving 35 agents. Consequently, the reported total training steps represent the cumulative steps taken by all agents combined.
Scene
citystreet
desert
village
downtown
lake
farmland
Steps (M)
19.2
13.4
21.3
19.8
9.9
9.2
Table 11: Transition steps across different scenes.
Scene
citystreet
desert
village
downtown
lake
farmland
T (M)
10.0
6.2
8.0
10.3
5.6
4.1
C.2 More Details
Network Structure. The structure of the GC-VAT is shown in Fig. 11 In this figure, C8x8-1654 represents 16 convolutional filters of size 8x8 and stride 4. GRU256 denotes a GRU network with 256 hidden units, and FC200 represents a fully connected layer with 200 neurons.
Domain Randomization. While simpler settings facilitate the agent's learning of task objectives, they also heighten the risk of the agent rapidly converging to a suboptimal action distribution, undermining the exploration process. Consequently, implementing domain randomization is essential. This is achieved through the randomization of the drone's s initial position and orientation relative to the target, necessitating broader range of actions maximize rewards. Moreover, to enhance the agent's spatial perception ability, randomization is also introduced in its gimbal pitch angle.
In two-stage curriculum learning process, employ identical domain randomization. The flight altitude is selected from the interval [13, m2)m, and the camera pitch angle is chosen from [0.6, 1.38]rad. These parameters are consistent throughout each episode. Mcanwhile, the drone's initial orientation relative to the target fluctuates within the range [-т, n raa, and the target's initial position is set between [-4.5,- 2.5] U [2.5, 4.5mm.
Details on coordinate transformations. Given two planes Po nigx =0 and P1 xinx along with the HTM Toi from Po to P1. The Toi is defined as:
Roi
101 1
To
(10)
Hence, the expression of plane P1 can be obtained using the analytical expression of plane Po and Toi as follows:
rē Roi T Dj Do riil..
(1I)
Considering the ground plane G. =- h in the world coordinate system {w), with representation in the camera coordinate system {c} denoted as G.
-0 0, the vectors of these
two planes are Po. (0. 0, 1, hh) and Pc. =Ag By Cg. Dy).
Furthermore, from Table g we can obtain the HTM Tew from {c} to {w} defined as follows:
T Rew
tea
(12)
where Rew is the rotation matrix from {c} to {w). which can be expressed in row vector form as: Rew [2, ral". Therefore, the homogeneous transformation matrix (HTM) T Twe. which represents the transformation from the world coordinate system [w] to the camera coordinate system {c), can be expressed as follows:
[n- 0
R Lew
The
(13)
Using Eq. 11 and the matrix Tues the plane G, can be formulated as Po. = - -h RT t
Privileged knowledge available for Drone Agent. During training in the simulator, the drone agent has access to additional information (e.g., the precise location of the target). However, during testing and real-world deployment, such privileged knowledge is not available.
22

---

Table 12: The detailed results of comparison experiments on CR metric.
Within Cross Scene
Cross Domain
Train: citystreet
citystrect
desert
village
downtown
lake
farmland
night
foggy
snow
AOT D-VAT GC-VAT
4943
4949
4545
4943
4843
4843
49
49_2
49_3
4848
46
46410
57411
5048
4643
48
54
53410
279+111
129 129
153+119
135 100
112492
191-122
157+
316484
202+119
Train: desert
citystrect
desert
village
downtown
lake
farmland
night
foggy
snow
AOT D-VAT GC-VAT
940
9+1
9+1
9+1
9.20
9400
9+1
9+1
9+1
51+10
47
46
564-11
39
4744
48413
48+13
+9+10
278 111
307
305494
119
=
275
182
307
307
Train: village
citystrect
desert
village
downtown
lake
farmland
night
Toggy
AOT D-VAT
51
51=11
4645
49
52=11
57.24
47
47:5
47=5
4648
4549
4448
694-42
4548
4543
4448
44-8
43
GC-VAT
234 112
119
+34
93
:53
:18
252
257
114
Train: downtown
citystrect
desert
village
downtown
lake
farmland
night
foggy
snow
AOT
5213
5249
4817
54
5315
5418
54g5
54-55
54
D-VAT GC-VAT
8+1
8+1
8+1
9+1
8+1
9+1
9+1
9+2
131
184+196
202-129
203+119
1894
223-114
167+135
165+126
178
Train: lake
citystroct
desert
village
downtown
lake
farmland
night
foggy
snow
AOT D-T GC-VAT
4943
49410
4645
4923
4743
4943
4843
48-4
48-3
504s
4549
45+10
704-42
464s
4342
4628
f1-s8
49
112-
144=110
203+133
1434134
181-116
214-111
190+129
1684110
997
Train: farmland
citystrect
desert
village
downtown
lake
farmland
night
foggy
snow
AOT D-VAT GC-VAT
51+7
5049
4645
49-3
51+9
60425
48-4
56424
56424
1342
1341
1341
1541
1441
1341
1441
13=11
14_1
189
170
237 118
81
159 119
217
1
245:1117
168
Table 13: The detailed results of comparison experiments on TSR metric.
Within Cruss Scene
Cross Domain
Train: citystreet
citystrect
desert
village
downtown
lake
farmland
night
foggy
snow
AOT VAT
0.20:0.02
0.24 0.22 0.25 02 0.23+0.05 0.24 0..55+0.02 0.25+0.02 0.24 0.27 10.04 0.1910.01 0.26610.02
0.2611002
0.25410.04 0.25 0.32
08
10,02 0.29
GC-VAT
+..0
0.54 0.50=0.32 0.45 0.44 0.66 0.72+0.29
0.93 0.79
Train: desert
citystrect
desert
village
downtown
lake
farnland
night
Foggy
snow
AOT D. VAT
0.06 0.064 0.06 0.06 0.06 0.06
0.06 0.06 0.06
00202
0.26++0.04 0.25 0.32+ 0.23++0us 0.26++0.01 0. 0.2644.00 0.26+0.04 0.26+0.04
GC-VAT
0.73
0. =0 0.38 0.56 0.82+0.25 0.57+
0.86 0.86
Train: village
citystrect
desert
village
downtown
lake
farnland
night
foggy
snow
AOT D-VAT
0.25 00205 0.204 0.23 0.244..02 0.25 0.26+4.00
0) 00.03 0.23 0.23
0.23
0.23-10.04 0.22=0.05 0.31 0.24 0.2240.01
0. 0.22+4.04 0.22+0.05 0.23
GC-
0.72
0.51 0.73 0.46 0.59 0.48
0.71 10.32
0.71 0.40
Train: downtown|
citystrect
desert
village
downtown
lake
farmland
night
foggy
snow
AOT D VAT
0.30
0.20
0.27 0.29 0.29 0.29 00.29+0.01 00.01 0.29
0.05 1.000 0.06 0.06 01.55 0.0611.00 0.06 0.00 0.06
GC-VAT
0.77
0.65 0.67 -0.29 0.65 0.49 +0.29
0.63 0.58 0..5 0.
Train: lake
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
AOT
0.25 0.25 0.23 0.24 0.24 0.24 0.244..01 0.24 0.24+0.01
D-VAT
0.2544.04 0.23+40.44 0.23-0.05 0.30+4.15 0.2644.06 0.22+0.01 0.2644.06 0.26 0,25
GC-VAT
0.43
0.47+0.30 0.64_0.31 0.43 0.61 0.59 0.59 0.62 00.1
Truin: farmland
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
AOT
10.02 0.24
0.22
0.25 0..04 0.23 0..3
0.20 0.23
D- VAT
0.07 0.07
0.07
0.08
0.0740.00
0.07 00.00
0.0840.00
0.07
0.08
GC-VAT
0.48 L0.24 0.59 10.34 0.72
10.20
0.58
0.68
10.32
0.78
0.51 20.28
Sparse Reward. In addition the dense reward function described in the main text, also provide a sparse reward function design. The sparse reward only provides a fixed reward when the target is within the image and no reward when it is outside. The definition of ra is as follows.
1, E
(14)
0,
otherwise
where I represents the image range. This reward can be used to construct the metric, Tracking Success Rate (TSR).
23

---

citystreet-day
desert-day
downtown-day
80
60
60
Reward
40
60
70
Reward
40
50
Revard
50
40
40
00
30
20
20
20
10
10
R-VAT
3.2
125
19.2
2.2
8.9
13.4
3,3
9,9 SM)p(M)
13.2
16.5
19.5
Step(M)
Step(M)
farmland-day
lake-day
village-day
50
70
H0
Reward
40
Reward
-0
Reward
60
-0
20
20
20
w/o CBT R-VAT
wio CBT R-VAT
wio CBT
R-VAT
3.5
14.2 Step(M)
10.6
21
Step(M)
Step(M)
Figure 13: Schematic diagram of reward curves on DAT scenes.
Training algorithm. For the training method of GC- VAT, we choose to use PPO algorithm. PPO algorithm regulates the speed of gradient updates by constraining the magnitude of policy changes rt. expressed as follows:
To (r((ass)) st)
(0)
(15)
Te (ox [s:)
where my and Teola are the new and old policies. Additionally, to enhance the agent's exploration, we introduce an entropy loss term H. formulated as:
H()r(s))
Σ no (alas) (all)))))
(16)
The optimization objective for the actor follows:
L Jmin (r. clip(r., --,, 1cc)..)++)))
(17)
where A, is the advantage function, is the clip parameter, and | is the entropy coefficient. The expression of A, is:
E A. Σ (7 8ets.
(18)
where T, X, de+z are the data collection step, generalized advantage estimator (GAE) [56] discount factor and temporal difference error respectively. The optimization objective expression of the critic network V is defined as:
Le Ea((re (V(+++1) V(s.))
(19)
The hyperparameters of the PPO algorithm used in this article are set as follows: discount factor 7 0.9, GAE discount factor À 0.95, entropy coefficient 3 0.01, PPO clipping parameter = 0.2.
Curriculum Learning for Agent Training. We introduce a Curriculum- Based Training (CBT) strategy designed to progressively enhance the performance of the tracker. In the first-stage curriculum, the agent is trained to track vehicles moving along straight trajectories without occlusions or extra interference. In the second-stage curriculum, and tasked with tracking targets exhibiting diverse and dynamic behaviors. The scenario of each stage shown in Fig. [2] where the upper row the first-stage environment, and the lower row corresponds to the second-stage environment.
agent is exposed to visually complex environments
24

---

Table 14: Effectiveness of CBT strategy the DAT benchmark, results from CR metric.
Within Cross Soene
Cross Domain
Train: citystreet
citystrect
desert
village
downtown
lake
farmland
night
foggy
snow
w/o CBT GC-VAT
5447
37+21
3046
30+14
48413
4844
5449
5449
5449
110
112
113
135
112
122
212
316454
218
Truin: desert
citystreet
desent
village
downtown
lake
farmland
night
foggy
snow
w/o CBT GC-VAT
253
302.99
284 305.94
175 102
236
266 121
241 1227
+79
306
111
307
119
170
1824131
307:1121
307
Train: village
citystreet
desert
village
downlown
farmland
night
foggy
snow
W/o CBT GC-VAT
230
127
258
59
126
120
267
208
73:168
122
160
2394134
4301
153
110
257
257
114
Train: downtown
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
w/o CBT GC-VAT
5449
49413
4748
57+15
5149
4844
2943
57+15
58
209
184+
202+122
119-119
189
224-111
167
165
178 126
Train: lake
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
w/o CBT
124
88
191+
93475
1874
198
1834110
185 1024
GC-VAT
812
144
203
143 134
181
214 111
190
168 99
Train: farmland
citystreet
desert
village
downtown
lake
farmlund
night
foggy
snow
w/o CBT GC-VAT
5219 162289
47 170
45.1
69
50a9
4642
4612
4613
4612
237
81
159
243=117
253
245
168 105
Table 15: Effectiveness of CBT strategy on the DAT benchmark, results from TSR metric.
Within Cross Scene
Cross Domain
Train: citystreet
citystreel
desert
village
downlown
lake
farmland
night
foggy
snow
w/o CBT
0.30
0.14 0.20 2. 0.28 06 0.21 0.30 0.30 ...000 0.30
GC-VAT
0.80+0.30
0.54 0.50 0.45=0.30 0.44+0.22 0.66+0.27 0.72+ 0.93 0.79
Train: desert
citystrect
desert
village
downtown
lake
farmland
night
foggy
snow
w/o CBT
0..24
4..5 0..6 0.52 +..94
0.74 0.59 +0.36
0.74+4..34 0.75
GC-VAT
0.73
0.84 1. 0.38 0.56 28 0.82 0.57=2.31
0.86 0.86
Train: village
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
w/o CBT
0.73 00.28 0.62 0.82 0.23 0.46 0.58 +0.33
0.71 +0.28
0.69
00.24
GC-VAT
0.72.0.28 28 0.5140.34 0.73 0.46 0.59
0.48
0.
20.32 0.71 0.40
Train: downtown
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
w/o CBT
0.29
0.27+0.03 0.2740.03 0.33
0.28220.5
0.27
0.33 0.33
0.33
GC-VAT
0.7740.31 4..5 0.67 0.65 4.49 0.63 0.58
40.31
0.65 0.64
Truin: lake
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
w/o CBT
0..30 0.47 0.45 0.44 0.57 28
0.59...20 0.78 10.22 0.62 0.33
GC-VAT
0.43 0.4740.30 0.64 0.43 0.61 0.59 0.59 0.6220.32 0.41
Train: farmland
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
w/o CBT
0.26
0.2440.04
0.23 2. 0.2622.06 0.23
0.23
0.23
0.23+
GC-VAT
0.48 0.59 0.72 0.33 0.58 10.28 0.68 0..32
0.67 40.32
0.78
0.51 20.28
D
Baselines
Active Object Tracking (AOT) 39. In this paper, the agent learns to follow a fixed target-tracking trajectory using A3C. In addition, the agent uses the following reward:
(y- + w))
(20)
where dl represents the optimal distance between the tracker and the target, is the maximum allowable distance, and denotes the maximum reward. In the original paper, c = 200 and A = 1.0. During replication,
set A 1.0, but due to the drone's 's camera being tilted downward, a value of e = 200 would far exceed the camera's 's field of view, which is unrealistic. Therefore, we modify the parameter e be the maximum offset distance that keeps the target within the image, i.e., 9.
D- [1]. this approach, the agent uses an asymmetric Actor-Critic network structure and the soft actor-critic learning method [25] to accomplish the task of drone tracking another drone. In the
25

---

Table 16: Effectiveness of reward design the DAT benchmark, results from CR metric.
Within Cross Scene
Cross Domain
Train: citystreet
citystrect
desert
village downtown
lake
farmland
night
foggy
snow
RA GC-VAT
9+1
8+1
840
8+1
940
940
9+1
941
9+1
110
112
153g11
135=100
112
121
257
316181
202:119
Train: desert
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
RD-VAT GC-VAT
921
9=0
821
9=0
840
10=a
8+1
1021
840
278
307+124
305
119
170+139
475
182+141
307 4124
307 +97
Train: village
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
RO-VAT GC-VAT
9+1
8+1
9
9=1
841
940
8+1
8
8
:32
160
239 #134
43
1534115
140
257 257+122
+120
115
Train: downtown
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
RD-VAT GC-VAT
811
840
8
9+11
841
8+1
9+1
9111
9.10
209
184
102
203-119
189
223
167
165
178 125
Train: lake
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
RI-VAT GC-VAT
l1
11
9+1
942
940
840
940
10+1
8
112
144
203
143
181
214 214
190 129
168
99 99.67
Train: farmland
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
RD-VAT GC-VAT
9+1
8+1
8+1
9+1
811
9+1
940
910
941
162
170
237
81471
1594119
243
253
245
168
Table 17: Effectiveness of reward design on the DAT benchmark, results from TSR metric.
Within Cross Scene
Cross Domain
Train: citystreet
citystreet
desert
village
downlown
lake
farmland
night
foggy
snow
RA GC-VAT
0.06 o0 0.0510.00 0.06 0.0620.00 0.06 00 0.06 0000 00.00 0.06 01 0.06
0.80 0.54 0.50 0.45-0.30 0.44 0.66 0.72 0.93 0.79
Train: desert
citystrect
desert
village
downtown
lake
farmland
night
foggy
snow
Ro-vAT GC-VAT
0.06
0.06 0.06+ 0.06 0.06 0.10 0. 2.000 0.09 0.06
0.731
0..2 0. 0.38 20.32 0.56 28 0.82 0.57 10.31 0.86 0.86
Train: village
citystreel
desert
village
downtown
lake
farmland
night
foggy
snow
Ro-var GC-VAT
0.06 0.06 0.00 0.06 0.05 00 0.06 0.03 0.06 0.06
0.72
0.51 0.73 0.46 0.59 0.48+ 0.71 40.32 0.71
20.29
Train: downtown
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
RO-VAT GC-VAT
0.06
0.06 0.06 0.06 0.05 00
0.06 0.06 0.06 0.06
0.77
40.5 00 0.65 0.49 0.63 0.58.4..0 0.65 0.64
Train: lake
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
RA GC-VAT
0.10 0.09 0.07 0.06 0.06 0.06 0.06
0.08
0.000
0.4340.25 0.47:0.30 0.64 0.43 0.61 0.59 0.59 0.6210.32 0.41
Train: farmland
citystreet
desert
village
downtown
lake
farmland
night
foggy
snow
RoSVar GC-VAT
0.06 0.05 0.05 0.06g0.01 0.05 0.06
0.06 0.0640.00
0.06+0.00 0.78
0.48 0.5940.34 0.72 0.33 0.58 40.2
0.68 +0.32
0.67 +0.32
0.51 20.28
actual comparative experiments, we convert it from a continuous action space to a discrete action space, referring [10]. Additionally, the method uses the following reward function.
r(k) (k. (k) ( (k) -ke
[
(21)
otherwise,
In the above equation Eq. 21 r. (1) and ru (k) are regularization terms for the drone's speed and output control, as shown in Eq. 22 For the discrete action space, the regularization term has fixed value for given action. This term only regularizes the linear velocity of the drone, which causes the drone to tend to perform rotational movements. Therefore, in the reproduction process, we set k, = and k = Additionally, due to the unexpectedly large acceleration values obtained for the target relative to the tracker under the discrete action setting, we set the input acceleration of the critic
26

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200
Target Center Position (py)
Ferwand Heckmard Left Right
150
100
50
-100
150
-200
-200
-150
-100
-50
50
100
150
Target Center Position (px)
Figure 14: Qualitative results on images from the caró video sequence. Arrows link data points the visualization of associated scenarios.
network to a(k)
[[((k]]] (k) [[(()]]]
[u(k)]] 1+ [R)]
(k))
(22)
is important note that in the AOT and D-VAT experiments, the target is initially positioned the center the tracker's image, and the initial forward directions of both the tracker and the target are aligned. Additionally, since the success criterion of DAT requires the agent keep the target the center of its view, the optimal distance between the tracker and the target is defined as the distance in the forward direction when the target is at the center of the camera's field of view. The tracker's flight altitude is set to 22 meters, and the gimbal pitch angle is 1.37 radians, which remains consistent with the parameters used during testing.
E
More Experiments
E.1
Experiment Settings
More Implementation Details. The training involves a range of 9.2M to 21.3M steps across 35 parallel environments. The webots runs at 500Hz, with the algorithm updating every four steps (125Hz). Episodes last up to 1500 steps and were terminated early the drone lost the target for over 100 consecutive steps, collided, crashed. The drone translation speed is set to 40m/s, and rotational speed to 2rad/s. The map features 40 vehicles, each with a maximum speed of 20m/s and accelcration of +25m/s². During testing, the altitude is set to 22m, the pitch angle to 1.37rad, and the target initializes at the camera's center.
The drone's translation speed is set to higher value to prevent it from becoming too similar to the target's 's speed (with maximum of 20 m/s). This prevents simple forward movement from yielding excessively high reward evaluations. If the drone's 'ss speed is set lower (e.g., 20 m/s), it may adopt suboptimal strategy, relying solely on one action.
Due to the varying challenges posed by different scene maps, the convergence speed of the agent differs across experiments. The training steps are shown in Table I0
Ablation Experiment Settings. this section, we introduce the training conditions of the single- stage RL and GC-VAT, well as the criteria for stage transitions. In single-stage RL, the agent placed in one six scenarios (citystreet, desert, village, downtown, lake, and farmland) for training. For GC-VAT, the agent is first trained in environment where randomly colored target moves straight along line without obstacles. After convergence, the model is then trained in the corresponding complex scenarios. The transition steps T for GC-VAT are in Table I1
E.2 Comparison Experiments
We provide comprchensive analysis of the comparative experimental results.
27

---

Tarret Center Position (py)
200
Table 18: Results (metrie is Correct Action
Eee
Rate) of 8 videos in VOT benchmark.
50
Video
carl
car3
caró
car8
Random
0.418 0.696
0.434
0.418
0.430
Ours
0.845
0.754
0.833
Video
carchase
carl6
following
car9
-200
-200
Target Center Position (px)
110
100
159
200
Random
0.129
0.421
0.314
0.139
Ours
0.870
0.834
0.773
0.756
Figure 15: Qualitative results on the car8 video.
Target Center Position (p))
300
Table 19: Results of videos in DTB70 benchmark.
Leit
Video
Car2
Car4
Car5
RoCar4
Random 0.419
0.421
0.429
0.436
Ours
0.757
0.894
0.893
0.876
Video
Car8
RaceCar RaceCarl
RoCar3
-386
-200
Target Center Position (px)
-100
100
200
200
Random 0.411
0.462
0.430
0.400
Ours 0.803
0.713
0.880
0.851
Figure 16: Qualitative results on the Car4 video.
Specifically, we provide detailed evaluations for within-scene (same scenes, same weather), cross- scene and cross-domain testing. Table reports the CRR metric of three models under cross-scene and cross-domain conditions, while Table 13 presents the TSR metric.
As shown in Table 12 and Table [3] the proposed GC-VAT significantly outperforms SOTA methods. Due to the reward design based on physical distance, both the AOT [39 and D- D-VAT [9] fail to accurately reflect the agent's is tracking performance from a top-down perspective (see Appendix C.I for theoretical proof), leading to misleading training signals for the tracker. Consequently, neither AOT nor D-VAT can effectively learn meaningful features, resulting in irregular performance distributions. In contrast, the proposed GC-VAT achieves superior convergence across all scenes. Specifically, in cross-scene experiments, the testing performance of the agent on the downtown map is relatively low, indicating that dense buildings and complex road elements pose significant challenges to the agent. Conversely, the testing performance on the village map is comparatively high, suggesting that the uniform color and simpler road conditions in the village map present fewer challenges.
For cross-domain testing experiments, the agent performs well under night and foggy conditions but struggles under snow conditions. This indicates that the proposed GC- VAT exhibits strong robustness to changes in lighting and visibility but is less adaptive to variations in scene tone.
E.3 Ablation Experiments
We present a comprehensive analysis of the ablation studies. First, we provide the reward curves for the citystreet, desert, village, downtown, lake, and farmland maps (see Fig. 13 Next, we provide detailed experimental results on the effectiveness of the Curriculum-Bai -ased Training strategy, as shown in Table [4]nd and Table L5
Finally, the effectiveness of the reward in the GC-VAT can be found in Table I6land Table 17
Effectiveness of reward design. To experimentally validate the effectiveness of the reward design
proposed in this paper and to corroborate the thcoretical proof in Appendix [.T] we conduct ablation experiments the reward function. The comparative method utilizes the reward function from [99 The detailed experimental results for the CR and T TSR metrics are provided in Table 16 and Table 17 For within-scene testing, the GC-VAT achieves an average improvement of 1 (0.06 + 0.72) in the TSR metric compared to the reward design in 19 In cross-scene and cross-domain testing, the GC-VAT achieves average enhancements of 850% - 0.57) and 1017%(0.0)
0.67) in the TSR metric, respectively. These results demonstrate the high effectiveness of the proposed reward.
Effectiveness of Curriculum-Based Training strategy. To validate the effectiveness of the proposed Curriculum- Based Training (CBT) strategy, we conduct ablation experiments by removing the CBT Inand module. The results for the CR and TSF metrics are presented in Table [4 and Table 15 respectively.
28

---

Table 20: Results of 8 videos in UAVIDT 20 benchmark.
Target Center Position (111)
300
Lef.
200
Video
S1603
S0201
S0101
S0306
100
Random
0.435
0.438
0.422
0.437
-300
Ours
0.896
0.806
0.865
0.773
6
Video
S/20/
S0303
S1301
S1701
-100
-300
Target Center Position (px)
-200
.100
100
200
300
400
Random 0.445
0.385
0.397
0.407
Ours
0.867
0.735
0.760
0.713
Figure 17: Qualitative results the RaceCarl video.
The experimental results demonstrate that single-stage reinforcement learning methods without the CBT strategy successfully learn task objectives and achieve convergence on the desert, village, and lake maps. These three maps exhibit similar environmental characteristics: the deserí and village maps feature uniform background colors and relatively simple road elements. Although the desert map has road segments partially covered by sand, these challenges are easy for the agent to overcome. Similarly, while the village map includes tunnels that may block vision, the proportion of tunnels is low. Additionally, although the lake map exhibits diverse background colors, the diversity primarily arises from vegetation-cover challenges for the agent. In contrast, single-stage reinforcement learning methods without the CBT strategy fail to converge on the citystreet, downtown, and farmland maps. This suggests that as the visual complexity of scenes and the density of elements increase, directly applying single-stage reinforcement learning is highly challenging and unlikely to converge. These results demonstrate the effectiveness of the CBT strategy.
areas, which occupy a small proportion of the map, resulting in low
Robustness under wind gusts and precipitation. the wind gust simulation experiments, we apply wind velocities in the range of [2.5, m.5mm/s for forward and lateral directions, and angular rate disturbances of [0.05, 0. D.d5/ around the yaw axis to mimic turbulence and gusts.
E.4 Experiments in Real-world Scenarios
We selected eight video sequences each from the VOT 30 DTB70 [35], and UAVDT 20 datasets to evaluate the transferability of GC-VAT. Specifically, from the VOT benchmark, we chose videos carl, car3, car6, car8, carchase, carl6. following, and car9. From the DTB70 benchmark, we selected Car2, Car4, Car5, RcCar4, Car8, RaceCar, RaceCarl, and RcCar3. From the UAVDT benchmark, chose S1603, S0201, S0101, S0306, S1201, S0303, S130I, and S1701. We provide qualitative visualizations for representative video sequences. Specifically, Fig. 14[shows the output actions for video in VOT 30] named car6. Fig. [5 shows the output actions for video in VOT named car8. Fig. [6 shows the output actions for a video in DTB70 named Car4. Fig. [7 shows the output actions for video in VOT named RaceCarl.
F Limitation
Although we validate the effectiveness of DAT and GC-VAT using real-world images and simple real-world scenarios, deploying the algorithm in truly open environments remains highly challenging. This is primarily due to the presence of numerous similar interfering objects and the high complexity of real-world conditions, which still exhibit a significant gap compared simulated environments. We will further enhance the algorithm's 's adaptability and conduct testing in real open- world environments.
29

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Reviews:
[{"review_id": "1qB5rN4bN1", "reviewer": "Reviewer_CwYq", "summary": "This paper presents DAT, a novel benchmark designed for open-world drone active air-to-ground tracking. The benchmark features high-fidelity simulation environments and human-like target behaviors, addressing the current lack of unified benchmarks and diverse environmental conditions in the drone tracking domain. In addition, the authors propose a goal-centered reward formulation that provides viewpoint-sensitive feedback to the agent.", "questions": "See the weakness. I think this paper to be a valuable contribution to the field of embodied AI and active perception. The new benchmark is timely and will likely benefit future research. The goal-centered reward is both novel and effective. Despite some limitations in baseline diversity and architecture choices, the paper demonstrates clear innovation, solid implementation, and meaningful insights. I recommend acceptance.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1.This paper is well-written and easy to follow. The motivation is clear. in current drone tracking setups, it lacks of environmental diversity and insufficient dynamic complexity. To overcome these limitation, this paper propose a new benchmark and a new method.\n\n2.The task of drone active tracking is well-define. Figure 2 effectively illustrates the attributes and diversity of the proposed benchmark.\n\n3.The proposed goal-centered reward is interesting. It enables the agent to reason about its viewpoints actively, which is essential in open-world scenarios. Extensive experiments valid the effectiveness of proposed components.\n\nWeaknesses\n\n1.The experiments include only two baseline methods. For a newly proposed benchmark to be impactful, a broader set of comparative evaluations, especially including more recent or state-of-the-art methods from the past 1-2 years would strengthen the empirical validation.\n\n2.The current implementation uses a CNN-based backbone. To further strengthen the empirical evaluation, I suggest to include comparisons with recent transformer-based architectures.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the thoughtful and constructive feedback, as well as for recognizing the contributions of our work. We greatly appreciate the reviewer’s acknowledgment of the novelty and impact of the DAT benchmark, the effectiveness of the GC-VAT method, and the rigor of our theoretical and experimental analysis. This encouragement motivates us to further advance research in open-world drone active tracking.\n\n>Q1. Include more recent or state-of-the-art methods from the past 1-2 years.\n\n**A1.** We sincerely thank the reviewer for the constructive suggestion. To validate our method against the latest advancements as suggested, we include two recent VAT methods published in ECCV 2024 [r1] and RAL 2024 [r2] under the same protocol. As shown in Table A, **GC-VAT consistently outperforms the baselines** across Tracking Success Rate (TSR) and Cumulative Reward (CR) metrics. The details are as follows:\n\n* **Additional Recent VAT Methods.** To strengthen the empirical evaluation as suggested, we further include two recent methods:\n    * **Offline RL (ECCV 2024)** [r1] integrates visual foundation models with offline reinforcement learning to enable data-efficient visual active tracking in embodied agents.\n    * **FAn (IEEE RAL 2024)** [r2], a pipeline-based approach that integrates visual tracking with PID control for improved stability in drone active tracking.\n* **Updated Empirical Evaluations.** These two methods reflect the most recent developments in VAT field. The extended comparisons in Table A show that GC-VAT significantly outperforms all the baselines across within-scene, cross-scene, and cross-domain settings.\n* **Original Selection of SOTA Methods.** In the original submission, we selected two widely recognized state-of-the-art methods in VAT: AOT [34] and D-VAT [17], with D-VAT representing the most recent and highest-performing approach prior to our work.\n\n\nTable A. Comparison between Offline RL and our method on DAT.\n\n| Metric: CR|Within-Scene↑|Cross-Scene↑|Cross-Domain↑|\n|-|-|-|-|\n|Offline RL [r1]|35±5|37±8|35±4|\n|FAn [r2]|28±3|31±5|33±8|\n|**Ours**|**279±110**|**144±111**|**258±110**|\n\n| Metric: TSR|Within-Scene↑|Cross-Scene↑|Cross-Domain↑|\n|-|-|-|-|\n|Offline RL [r1]|0.09±0.01|0.10±0.02|0.09±0.01|\n|FAn [r2]|0.16±0.02|0.16±0.01|0.18±0.05|\n|**Ours**|**0.80±0.30**|**0.52±0.29**|**0.82±0.23**|\n\n\n\n[r1] Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL, ECCV 2024.\n\n[r2] Follow anything: Open-set detection, tracking, and following in real-time, RAL 2024.\n\n>Q2. Include comparisons with recent transformer-based architectures.\n\n**A2.** We have incorporated experiments comparing our CNN-based method with MobileViT [r3]. Our results indicate that the MobileViT-based method underperforms the adopted CNN-based approach in tracking performance and inference efficiency during reinforcement learning training.\n\n* **Comparisons with Lightweight Transformer.** To directly address the reviewer’s suggestion, we conduct additional experiments incorporating a MobileViT [r3] backbone into our evaluation. We choose MobileViT specifically for its focus on efficiency and edge-device applicability. As detailed in Table B and Table C, the adopted CNN-based model achieves a **65% higher average Complete Rate (CR)** within-scene compared to MobileViT. Crucially, it also delivers **6.6x faster inference speed**. In our experiments on DAT, we observe that MobileViT exhibits slower convergence and higher variance in the reward curve. This indicates challenges in learning stable and optimal policies, aligning with observations in [r4] regarding training instability in similar RL settings.\n* **The Necessity of CNNs for Edge Deployment in Visual Active Tracking.** Drone-based visual active tracking should operate on resource-constrained edge devices (e.g., onboard computers or embedded systems). This environment demands extreme efficiency: minimal computational cost, low memory footprint, and real-time inference latency are non-negotiable for reliable and safe operation. CNN architectures are the established default backbone precisely because they excel in meeting these critical constraints: they are computationally efficient, inherently lightweight, and deliver the fast inference essential for real-time tracking. This fundamental alignment with deployment realities is why CNNs dominate VAT systems [r5, r6].\n\nTable B. Comparisons between transformer-based and CNN-based model on Citystreet-day map of DAT.\n| Metric: CR|Within-Scene↑|Cross-Scene↑|Cross-Domain↑|\n|-|-|-|-|\n|MobileVit[r3]|169±73|47±21|45±25|\n|**CNN**|**279±110**|**144±111**|**258±110**|\n\n| Metric: TSR|Within-Scene↑|Cross-Scene↑|Cross-Domain↑|\n|-|-|-|-|\n|MobileVit[r3]|0.35±0.21|0.11±0.04|0.15±0.12|\n|**CNN**|**0.80±0.30**|**0.52±0.29**|**0.82±0.23**|\n\nTable C. Results of inference time on an RTX 3090 GPU\n\n|Architecture|Inference Time (s)↓|\n|-|-|\n|MobileVit[r3]|0.0106|\n|**CNN**|**0.0016**|\n\n[r3] Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer, ICLR 2022.\n\n[r4] On transforming reinforcement learning with transformers: The development trajectory, TPAMI 2024.\n\n[r5] D-vat: End-to-end visual active tracking for micro aerial vehicles, RAL 2024.\n\n[r6] End-to-end active object tracking and its real-world deployment via reinforcement learning, TPAMI 2019.\n\n\n****\nWe sincerely hope our clarifications above have addressed your concerns. We would be grateful if you could kindly reconsider the evaluation of our paper."}, {"review_id": "E0E7XjBHeh", "reviewer": "Reviewer_e5dU", "summary": "This paper presents GC-VAT, a comprehensive reinforcement learning solution with goal-centered rewards for open-world drone active tracking. The authors develop DAT, the first benchmark for drone active air-to-ground tracking, featuring 24 city-scale scenes with high-fidelity dynamics simulation and tools for generating human-like target behaviors and unlimited scenes. To enhance tracking in complex environments, they design a Goal-Centered Reward function providing precise viewpoint feedback and a Curriculum-Based Training strategy for progressive agent adaptation. Experiments on simulator and real-world images demonstrate GC-VAT achieves 400% improvement over state-of-the-art methods in cumulative reward metrics, validating the method's robustness and generalization across diverse dynamic scenarios.", "questions": "1. The paper only evaluates the model on the VOT and DTB70 datasets and draws conclusions about its effectiveness in real-world images based on the average correct action rate from just five videos, which presents certain limitations. To strengthen the persuasiveness of the conclusion, the paper should expand the test dataset range, report the average correct action rates for each dataset individually, and demonstrate the model's generalizability by showing consistent performance across multiple datasets.\n\n2. The complexity of some of the scene constructions is insufficient. For example, the village map in the paper presents mountainous terrain with tunnels, but the road surface remains flat and unaffected by the terrain, making the distinction between mountainous and flat areas minimal. It is recommended to increase the scene’s complexity, particularly with regard to obstacles and terrain features.\n\n3. The paper provides limited discussion on common external disturbances in real-world environments, such as gusts, wind turbulence, and the attenuation effects of precipitation on sensors. It is recommended to include further analysis or experimental validation on these aspects.\n\n4. The arrow in Figure 13 should be explained in the following text to help with understanding.\n\n5. It is recommended to add a color bar to the reward contour plot in Figure 4 to help readers better understand the value range.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. The DAT proposed in this paper is the first open-world benchmark for drone active aerial-to-ground tracking. It includes 24 city-scale scenes, featuring high-fidelity dynamic simulations and human behavior models, providing a comprehensive and realistic testing environment for algorithm validation.\n2. By designing a Goal-Centered Reward function and a Curriculum-Based Training strategy, GC-VAT significantly improves tracking performance in complex dynamic environments. Experiments show that its cumulative reward metric is approximately 400% higher than existing methods.\n3. The paper not only proposes a new reward design approach but also validates its effectiveness through theoretical analysis and experimental verification. Particularly, its ability to provide precise feedback from different perspectives enhances the credibility of the method.\n\nWeaknesses\n\n1. The paper tests the model only on the VOT and DTB70 datasets, and its effectiveness in real-world images is evaluated based on the average correct action rate from only a small number of videos, which raises concerns about the generalizability of the conclusions.\n2. The experimental evaluation lacks consideration of real-world environmental challenges, including both external disturbances (e.g., wind gusts, turbulence, and precipitation effects on sensors) and realistic terrain complexity (e.g., overly simplified village map with flat roads), potentially compromising the method's practical applicability.", "was_revised": true, "final_justification": "Most of my concerns have been addressed.", "rebuttal": "We sincerely appreciate your recognition of our work in developing the first open-world benchmark for drone active aerial-to-ground tracking, as well as the acknowledgment of our innovative reward design and training strategy. Your feedback on the effectiveness and credibility of our approach is truly valuable to us.\n\n>Q1. The paper should expand the test dataset range, report the average correct action rates for each dataset individually.\n\n**A1.** We thank the reviewer for the valuable feedback. We have extended our evaluation as suggested on real-world images and successfully **deployed the GC-VAT policy on a physical DJI Mini 3 Pro drone** to validate its consistent tracking capability in real-world scenarios. This real-world deployment demonstrates the practical effectiveness and robustness of our method beyond simulation.\n\n* **Expanded Real-World Image Evaluations**. We have expanded the number of test videos on real-world image datasets. Specifically, we now evaluate zero-shot transfer performance on 8 videos from VOT [26], 8 from DTB70 [30], and 8 from UAVDT [r1], expanding the original evaluation scale. The results in Table A-C show consistently strong performance across all datasets:\n    * **VOT**: Average Correct Action Rate (CAR) = **0.795**\n    * **DTB70**: Average CAR = **0.833**\n    * **UAVDT**: Average CAR = **0.802**\n    \n    This consistent performance across diverse datasets with varying environmental conditions, camera motions, and target dynamics confirms the strong generalization ability of GC-VAT.\n    \n* **Real-World Drone Deployment**. Furthermore, as a critical step beyond image-based evaluation, we deploy the GC-VAT policy on a DJI Mini 3 Pro drone for real-world vehicle tracking. GC-VAT demonstrates strong robustness without any fine-tuning, despite real-world challenges such as airflow, sensor noise, and communication latency. As shown in Table D, across **four test trajectories**, GC-VAT achieves an **average zero-shot Tracking Success Rate of 88.4%** and an **average Correct Action Rate of 81.3%**. This successful zero-shot Sim-to-Real transfer validates the realism of the DAT benchmark and the practical applicability of our approach. \n\n    We acknowledge that visualizations of Sim2Real deployment would improve clarity. However, due to NeurIPS rebuttal policy, images and videos are not permitted at this stage. We will include  frames and clips in the revised manuscript.\n\n\nTable A. Results (metric is Correct Action Rate) of 8 videos in VOT benchmark.\n|Video|car1|car3|car6|car8|carchase|car16|following|car9|\n|-|-|-|-|-|-|-|-|-|\n|Random|0.418|0.434|0.418|0.430|0.429|0.421|0.314|0.439|\n|**Ours**|**0.696**|**0.845**|**0.754**|**0.833**|**0.870**|**0.834**|**0.773**|**0.756**|\n\nTable B. Results (metric is Correct Action Rate) of 8 videos in DTB70 benchmark.\n|Video|Car2|Car4|Car5|RcCar4|Car8|RaceCar|RaceCar1|RcCar3|\n|---|---|---|---|---|---|---|---|---|\n|Random|0.419|0.421|0.429|0.436|0.411|0.462|0.430|0.400|\n|**Ours**|**0.757**|**0.894**|**0.893**|**0.876**|**0.803**|**0.713**|**0.880**|**0.851**|\n\nTable C. Results (metric is Correct Action Rate) of 8 videos in UAVDT [r1] benchmark.\n|Video|S1603|S0201|S0101|S0306|S1201|S0303|S1301|S1701|\n|---|---|---|---|---|---|---|---|---|\n|Random|0.435|0.438|0.422|0.437|0.445|0.385|0.397|0.407|\n|**Ours**|**0.896**|**0.806**|**0.865**|**0.773**|**0.867**|**0.735**|**0.760**|**0.713**|\n\nTable D. Results of real-world drone deployment. AOT [34] and D-VAT [17] fail to learn effective policies even in simulation, and therefore cannot be successfully deployed on real drones.\n|Metric|Tracking Success Rate|Correct Action Rate|\n|-|-|-|\n|AOT|-|-|\n|D-VAT|-|-|\n|**Ours**|**88.4%±8.0%**|**81.3%±1.4%**|\n\n[r1] The unmanned aerial vehicle benchmark: Object detection and tracking, ECCV 2018.\n\n>Q2. The village map in the paper presents mountainous terrain with tunnels, but the road surface remains flat and unaffected by the terrain, making the distinction between mountainous and flat areas minimal. It is recommended to increase the scene’s complexity, particularly with regard to obstacles and terrain features.\n\n**A2.** To directly address the reviewer’s suggestion, we leverage DAT’s digital twin tool to upgrade terrain realism, and validate GC-VAT’s zero-shot generalization capability on unseen terrains.\n\n* **Enhancing Terrain Realism with the Digital Twin Tool.** DAT benchmark includes a **digital twin tool** (detailed in Section 3.1 and Appendix B) that supports the integration of real-world elevation data, enabling the generation of scenes with realistic, elevation-aware terrain and road surfaces. In response to the reviewer’s suggestion, we will enhance the benchmark in the revised manuscript by explicitly incorporating terrain complexity metrics and providing updated scenes with sloped roads and elevation variations. We believe this will further strengthen the realism and utility of DAT for future research.\n\n* **Generalization to Real-World Terrain Variations**. Notably, GC-VAT, trained on flat roads in simulation, achieves zero-shot generalization to real-world scenarios with significant elevation changes and sloped terrains. This is enabled by the goal-centered reward (in Section 4.2), which accurately reflects the target’s position in the image and decouples policy learning from specific terrain geometry. As shown in Table 5 in the paper, GC-VAT achieves a **Correct Action Rate of 0.880** on the challenging RaceCar1 sequence in the DTB70 dataset, where the target moves on a track with pronounced slopes and elevation variations.\n\n\n>Q3. The paper provides limited discussion on common external disturbances in real-world environments, such as gusts, wind turbulence, and the attenuation effects of precipitation on sensors. It is recommended to include further analysis or experimental validation on these aspects.\n\n**A3.** We conduct rigorous tests under wind gusts and sensor degradation caused by precipitation. Results in Table E and Table F demonstrate that GC-VAT maintains stable performance under the interference. \n\n* **Resilience to Wind Perturbations.** To simulate realistic wind effects, we introduce randomized perturbations along the forward, lateral, and yaw directions during testing. Specifically, we apply wind velocities in the range of [2.5, 7.5] m/s for forward and lateral directions, and angular rate disturbances of [0.05, 0.15] rad/s around the yaw axis to mimic turbulence and gusts. The results under such wind disturbances are summarized in Table E, where the model is trained on citystreet-day and evaluated on citystreet-foggy with added wind perturbations. The Tracking Success Rate (TSR) drops by less than 0.06, demonstrating that GC-VAT maintains strong robustness under significant wind disturbances.\n* **Visual Robustness under Precipitation.** Since our method relies solely on monocular RGB images for decision making, precipitation primarily affects tracking through two mechanisms: visibility attenuation and blurring caused by raindrops. \n    * **Visibility Attenuation:** The robustness of our method under fog-induced visibility degradation is already demonstrated in existing experiments, such as the cross-domain evaluation on citystreet-foggy. As shown in Table 2 and Table 3 of the paper, GC-VAT achieves a tracking success rate (TSR) with only a 0.04 drop (0.80→0.76) when transferring from clear to foggy conditions. This small performance gap indicates that **GC-VAT effectively adapts to visibility attenuation caused by precipitation-related fog**.\n    * **Blurring Caused by Raindrops:** We follow established practices in test-time adaptation literature [r2] to simulate realistic rainfall effects. Specifically, we train the policy on citystreet-day map and evaluate under synthetically generated rain in within-scene, cross-scene, and cross-domain settings. To ensure realism, we exclude snowy conditions from the cross-domain evaluation, as snow and rain rarely co-occur in real-world environments. The results in Table F show only marginal performance degradation (less than 0.07 in Tracking Success Rate) under rain simulation, confirming that **GC-VAT is robust to blurring caused by raindrops**.\n\nTable E. Performance under wind disturbances and clean conditions.\n|Exp_Name|CR|TSR|\n|-|-|-|\n|w/ Forward|302±94|0.91±0.18|\n|w/ Lateral|304±82|0.91±0.19|\n|w/ Yaw|301±120|0.88±0.23|\n|**w/o Wind**|**316±84**|**0.94±0.14**|\n\nTable F. Performance under rainy and clean conditions.\n|Metric: CR|Within-Scene|Cross-Scene|Cross-Domain|\n|-|-|-|-|\n|w/ rain|266±110|139±109|274±103|\n|**w/o rain**|**279±110**|**144±111**|**287±105**|\n\n|Metric: TSR|Within-Scene|Cross-Scene|Cross-Domain|\n|-|-|-|-|\n|w/ rain|0.74±0.29|0.45±0.30|0.77±0.29|\n|**w/o rain**|**0.80±0.30**|**0.52±0.29**|**0.83±0.22**|\n\n[r2] Heavy rain image restoration: Integrating physics model and conditional adversarial learning, CVPR 2019.\n\n>Q4. The arrow in Figure 13 should be explained in the following text to help with understanding.\n\n**A4.** In Figure 13, the arrow indicates the visualization of data points. For instance, the yellow point in the upper right corner represents a target point located to the right of the image center, while the arrow points to the corresponding actual image.\n\n>Q5. It is recommended to add a color bar to the reward contour plot in Figure 4 to help readers better understand the value range.\n\n**A5.** Thank you for your suggestion. We have incorporated the modifications in the revised version of our manuscript.\n\n****\nWe sincerely hope our clarifications above have addressed your concerns. We would be grateful if you could kindly reconsider the evaluation of our paper."}, {"review_id": "VeQy6jakYx", "reviewer": "Reviewer_92vZ", "summary": "This paper focuses on the problem of active visual tracking of drones in an open world environment, and proposes the first relevant benchmark dataset DAT and a reinforcement learning-based method GC-VAT. DAT contains 24 urban scenes, with human-like target behavior and realistic dynamic simulation, and a digital twin tool that can generate scenes infinitely. GC-VAT designs a target center reward function to accurately feedback the target position, and also introduces a curriculum training strategy to improve performance in complex environments. Experiments show that it performs significantly better than existing methods in simulator and real image tests.", "questions": "1. Although DAT provides human-like behavior goals, in actual applications, the target behavior may be more complex and unpredictable. How does the GC-VAT method handle these situations?\n2. In an open world environment, drones may encounter dynamic obstacles. How does the GC-VAT method handle these dynamic obstacles?\n3. How to solve the problem that the curriculum training strategy may be unstable during the transition from a simple environment to a complex environment?\n4. How does the reward function handle when the target is surrounded by multiple similar objects?\n5. The parameter selection of domain randomization and the division of environmental complexity in the curriculum training strategy are relatively complex, and there is room for further optimization and improvement", "limitations": "No", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Advantages:\n1. The DAT benchmark dataset proposed in the paper contains a rich variety of scenes and target types, and its effectiveness and practicality are verified through simulator and real-world image experiments. At the same time, the GC-VAT method performs well in the experiment, and has significant improvements in key indicators such as cumulative rewards compared to existing methods, proving the effectiveness of the method.\n2. Starting from the actual needs of the UAV visual active tracking problem, the article systematically analyzes the shortcomings of existing methods, and then proposes the DAT benchmark dataset and GC-VAT method, and elaborates on the design ideas, implementation details and theoretical basis of the two, so that readers can clearly understand the motivation, methods and contributions of the research.\n3. DAT is the first open-world UAV active tracking benchmark dataset. Its rich scenes, high-fidelity dynamics simulation and unlimited scene generation capabilities provide a powerful tool for research in the field of UAV tracking, which will help promote further development and innovation in this field.\n4. The target-centered reward function and curriculum training strategy in GC-VAT are innovative. The target center reward function can accurately feedback the position of the target in the image and guide the drone to keep the target centered, solving the problem of inaccurate feedback in different perspectives of existing methods; the course training strategy effectively improves the performance of the model in complex environments.\n\nDisadvantages:\n1. Although experimental verification has been conducted on simulators and real-world images, the paper has not been fully tested on real drones. Therefore, it is impossible to fully evaluate the performance and robustness of the GC-VAT method in real environments, especially when facing complex factors such as airflow interference and sensor noise that real drones may encounter.\n2. Although DAT contains a variety of urban-scale scenes, the environment in the real world is complex and changeable, and there are some special scenes or extreme cases that are not covered, resulting in insufficient generalization ability of GC-VAT in these unseen scenes, affecting the breadth of its practical application.\n3. In the implementation process of GC-VAT, such as the parameter selection of domain randomization and the division of environmental complexity in the curriculum training strategy, there is room for further optimization and improvement to further improve the performance and efficiency of the method.\n4. Although the target center reward function can effectively guide the drone to keep the target centered in most cases, in some complex scenarios, such as when the target is surrounded by multiple similar objects, the reward function may not be able to accurately distinguish the target object, resulting in tracking failure.\n5. The curriculum training strategy may experience training instability during the transition from simple to complex environments.\n6. Although DAT provides human-like behavior targets, in actual applications, target behaviors may be more complex and unpredictable. The GC-VAT method requires further training and adjustment to maintain good tracking performance when facing these unseen target behaviors.\n7. In an open world environment, drones may encounter dynamic obstacles. However, the paper does not discuss in detail how the GC-VAT method handles these dynamic obstacles.\n8. As task requirements change and new scenarios emerge, the GC-VAT method may need to be expanded or modified. However, the paper does not discuss in detail the scalability and modular design of the model, which may require the entire model to be redesigned and trained when facing new tracking tasks or environments, increasing the difficulty and cost of application.", "was_revised": false, "final_justification": "", "rebuttal": ">Q1. The paper has not been fully tested on real drones.\n\n**A1.** We sincerely thank the reviewer for the insightful comment. We have **successfully conducted real drone deployment**.\n* **Real-World Drone Deployment**. We deploy the GC-VAT policy on a DJI Mini 3 Pro drone to actively track a moving vehicle. We use DJI’s Mobile SDK to stream live video to a computer for policy inference and to send control actions back to the drone for real-time execution, forming a closed-loop system.\n* **Zero-Shot Sim-to-Real Deployment and Results.** GC-VAT demonstrates strong robustness without any fine-tuning, despite real-world challenges such as airflow, sensor noise, and communication latency. As shown in Table A, across **four test trajectories**, it achieves an **average zero-shot Tracking Success Rate of 88.4%** and an **average Correct Action Rate of 81.3%**, demonstrating its strong effectiveness in real-world conditions. \n\nWe acknowledge that visualizations of Sim2Real deployment would improve clarity. However, due to NeurIPS rebuttal policy, images and videos are not permitted at this stage. We will include  frames and clips in the revised manuscript.\n\nTable A. Results of real-world drone deployment. AOT [34] and D-VAT [17] fail to learn effective policies even in simulation, and therefore cannot be successfully deployed on real drones.\n|Metric|Tracking Success Rate|Correct Action Rate|\n|-|-|-|\n|AOT|-|-|\n|D-VAT|-|-|\n|**Ours**|**88.4%±8.0%**|**81.3%±1.4%**|\n\n>Q2. GC-VAT lacks sufficient generalization in unseen scenes due to uncovered special and extreme cases.\n\n**A2.** We choose dense fog as a representative extreme case. As shown in Table B, GC-VAT maintains strong performance under such conditions without retraining.\n* **Example of Extreme Cases**. According to Chinese national standards (GB/T 27964-2011) [r1], visibility below 200 meters is classified as dense fog, posing significant risks to UAV operations. We use 200m visibility as a representative extreme case.\n\n* **Additional Experiment and Results**. We train GC-VAT on daytime data and test it under 200m visibility. From Table B, GC-VAT maintains stable performance, with only a marginal drop in Tracking Success Rate (0.94→0.90), indicating reasonable generalization to extreme cases.\n\nTable B. Results on citystreet-foggy map under extreme cases.\n\n|Visibility(m)|CR|TSR|\n|-|-|-|\n|200|298±102|0.90±0.15|\n|**1000(Original)**|**316±84**|**0.94±0.14**|\n\n[r1] GB/T 27964-2011: Forecasting Grades of Fog, 2011.\n\n>Q3. The parameter selection in domain randomization and curriculum training can be further optimized.\n\n**A3.** We provide additional ablation studies to further justify our parameter selection and curriculum strategy.\n\n* **Domain randomization parameters are empirically optimal**. For domain randomization, we test height ranges: [13,22] (ours), [17,22], and [21,22]. As shown in Table C, our range [13,22] achieves the highest performance, validating our parameter selection.\n* **Curriculum design has room for improvement, but our strategy offers an efficient balance.** We compare our curriculum strategy with a three-stage variant: (1) fixed-color targets; (2) varying color targets; (3) full complexity. From Table D, it achieves slightly higher Cumulative Reward (CR) than our method in within-scene and cross-scene settings, confirming potential for improvement. Nevertheless, our approach attains strong performance with simpler design.\n* **Superior performance is clear**. Despite potential for further tuning, our method already significantly outperforms existing SOTA approaches, demonstrating the effectiveness of our current design.\n\nTable C. Ablation studies on height domain randomization.\n|Height Random/Metric: CR|Within-Scene|Cross-Scene|Cross-Domain|\n|-|-|-|-|\n|[17,22]|221±95|112±87|187±111|\n|[21,22]|174±60|122±75|147±66|\n|**[13,22] (Ours)**|**279±110**|**144±111**|**287±105**|\n\n|Height Random/Metric:TSR|Within-Scene|Cross-Scene|Cross-Domain|\n|-|-|-|-|\n|[17,22]|0.68±0.20|0.48±0.27|0.65±0.24|\n|[21,22]|0.50±0.36|0.46±0.22|0.49±0.17|\n|**[13,22] (Ours)**|**0.80±0.30**|**0.52±0.29**|**0.82±0.23**|\n\nTable D. Comparisons between the two-stage curriculum learning (Ours) and a three-stage variant\n|Metric: CR|Within-Scene|Cross-Scene|Cross-Domain|\n|-|-|-|-|\n|3-stage CL|**329±75**|**153±120**|239±112|\n|Ours|279±110|144±111|**258±110**|\n\n|Metric:TSR|Within-Scene|Cross-Scene|Cross-Domain|\n|-|-|-|-|\n|3-stage CL|**0.87±0.24**|0.51±0.32|0.72±0.29|\n|Ours|0.80±0.30|**0.52±0.29**|**0.82±0.23**|\n\n>Q4. When the target is surrounded by similar objects, the reward function may fail to distinguish it.\n\n**A4.** We respond to this concern from two perspectives: (1) the target-specific design of the reward function and (2) the empirical robustness of GC-VAT under visually similar distractors.\n\n* **Target-awareness in the reward function.** Our reward function uses the target’s ground-truth position, which is widely accepted in VAT [17,34]. Therefore, even with visually similar distractors, the reward signal reliably corresponds to the designated target. \n* **Empirical robustness under visually similar distractors.** To further evaluate GC-VAT’s robustness to distractors, we introduce a similar-looking vehicle near the target. From Table E, our model maintains high tracking performance, indicating effective discrimination of the true target despite confusing objects.\n\nTable E. Results of similar-looking vehicles on citystreet-day map.\n|Distractor Number|CR|TSR|\n|-|-|-|\n|1|263±133|0.76±0.24|\n|**0 (Original)**|**279±110**|**0.80±0.30**|\n\n>Q5. The curriculum training strategy may experience training instability.\n\n**A5.** We would like to emphasize that our Curriculum-Based Training (CBT) strategy is designed to prevent instability, and both its mechanism and empirical results confirm stable transitions without performance drops or oscillations.\n\n* **Our CBT design ensures stable transitions.** As detailed in Section 4.3 and Algorithm 1, the transitions follow a convergence criterion rather than fixed steps: training progresses only when the reward stabilizes above a threshold η. This ensures that the agent learns a robust policy before exposure to complexity (e.g., occlusions), preventing training instability.\n\n* **Training is smooth and monotonic.** Figure 5 in the paper shows the reward curves rise steadily across all scenes, with no drop or oscillation at the curriculum transition point. This demonstrates a stable and seamless curriculum transition.\n\n>Q6. The GC-VAT method requires further training when facing these unseen target behaviors.\n\n**A6.** We would like to clarify that GC-VAT, trained on the diverse behaviors in DAT, inherently generalizes to unseen target behaviors without requiring further training.\n\n* **Robustness under Rare or Extreme Behaviors.** As mentioned in Section 6.4, we perform real-image experiments on the RaceCar1 video from DTB70 dataset, featuring extreme target behaviors like drifting and serpentine motion. Table 5 in the paper shows GC-VAT achieves zero-shot generalization to unseen behaviors with a **Correct Action Rate of 0.880**.\n* **DAT Captures Common Real-World Target Behaviors.** As shown in Figure 2, DAT includes diverse target behaviors, such as lane changes. These maneuvers cover the majority of real-world driving behaviors [r2]. This diversity enables GC-VAT to learn robust policies that generalize to unseen behaviors without further training.\n\n[r2] The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems, ITSC 2018.\n\n>Q7. The paper does not discuss in detail how the GC-VAT method handles the dynamic obstacles.\n\n**A7.** GC-VAT method handles dynamic obstacles through collision-aware reset mechanism.\n\n* **Collision-Aware Reset Mechanism**. Our training framework encourages the drone to avoid both static (buildings, trees) and dynamic obstacles (drones, vehicles). If a collision occurs during training, the episode is immediately reset with a strong penalty. Therefore, the drone learns visual obstacle avoidance for higher rewards.\n\n>Q8. Insufficient discussion on model scalability and modular design limits adaptability to new tasks or environments.\n\n**A8.** We clarify that GC-VAT is scalable and generalizable, as evidenced by: (1) a general-purpose reward function, (2) a model-agnostic curriculum strategy, and (3) empirical generalization to unseen targets and environments.\n* **General-Purpose Reward Design.** The proposed goal-centered reward function (detailed in section 4.2) is applicable to any VAT setting that involves viewpoint variation and image-based tracking. It only requires the target’s image-plane projection and no task-specific heuristics, enabling accurate target localization under varying viewpoints.\n\n* **Model-Agnostic Curriculum Training.** Our curriculum learning strategy is independent of GC-VAT’s architecture. It can be easily applied to any VAT policy, as it relies only on the agent’s performance (e.g., average reward) to adaptively schedule training difficulty. This design enhances agent performance and accelerates learning in complex environments.\n\n* **Empirical Validation of Generalization.** From Table F, our model maintains strong performance when facing an unseen target (Bus). Moreover, Tables 2 and 3 in the paper show that GC-VAT achieves cross-scene and cross-domain transferability. These results show that our method can adapt to new tasks and environments.\n\nTable F. Results of unseen targets (Bus).\n|Metric: CR|Within-Scene|Cross-Scene|Cross-Domain|\n|-|-|-|-|\n|Target: Bus (unseen)|229±92|131±89|207±94|\n|**Ours**|**279±110**|**144±111**|**258±110**|\n\n|Metric: TSR|Within-Scene|Cross-Scene|Cross-Domain|\n|-|-|-|-|\n|Target: Bus (unseen)|0.79±0.25|0.50±0.33|0.79±0.27|\n|**Ours**|**0.80±0.30**|**0.52±0.29**|**0.82±0.23**|\n\n****\nWe sincerely hope our clarifications above have addressed your concerns."}, {"review_id": "8chSumXUVt", "reviewer": "Reviewer_Vwhj", "summary": "In this paper, the authors studied the problem of Visual Active Tracking (VAT) on drones. The authors presented a new open-world drone active air-to-ground tracking benchmark named DAT that (1) includes many different large-scale cases and can add new scenes automatically, (2) is capable of simulating full dynamics with human-like target movement, realistic setting, and can deal with both aerial and ground robots. On top of the new benchmark, a VAT algorithm called GC-VAT is proposed, which utilizes goal-centered rewards and leverages a Curriculum-Based Training strategy to achieve better tracking performance. The results are validated in experiments with different scenes.", "questions": "See above", "limitations": "No negative societal impact need to be discussed", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength: \n1. This paper is well-motivated, clearly written and organized. \n2. The workload of this paper is significant. The proposed benchmark DAT is realistic and generalizable, which can contribute to the field. The proposed GC reward offered significant improvement compared with SOTA in the experiments on DAT. \n\nWeakness: \n1. The performance of GC-DAT with SOTA methods are evaluated in the proposed benchmark DAT, which contains many new properties that the experiment environments of the previous works did not have. These properties are useful, but the performance of the proposed method would be more reliable if the authors could conduct the experiments and compare the performance in those experiments, or remove the properties (like weather and complex dynamics, which are not modelled in previous works)\n2. The font of the texts in the figures are too small to read\n3. Although in line 298, the authors mentioned that testing the performance on real images is a method used by previous work. However, since the proposed benchmark is pretty realistic and models many properties in the real world, it would be especially interesting if GC-VAT could be tested in the real world on real robots. In this way, the significance of this work and the generalization capability of the benchmark can be better demonstrated.", "was_revised": true, "final_justification": "The authors' rebuttal has resolved my concerns.", "rebuttal": "We greatly appreciate your recognition of the motivation, clarity, and significance of our work, as well as the acknowledgment of the improvement brought by our GC reward in the experiments on DAT.\n\n>Q1. The performance of GC-DAT with SOTA methods are evaluated in the proposed benchmark DAT, which contains many new properties that the experiment environments of the previous works did not have. These properties are useful, but the performance of the proposed method would be more reliable if the authors could conduct the experiments and compare the performance in those experiments, or remove the properties (like weather and complex dynamics, which are not modelled in previous works).\n\n**A1.** We appreciate the valid concern regarding benchmark fairness. We clarify the reliability of our experimental comparisons, demonstrating the superiority of our method, and strengthening our empirical validation as follows:\n\n* **Fairness of experimental comparisons.** The new properties modeled in the DAT benchmark, such as multiple weather conditions and complex visual occlusions (e.g., trees and tunnels), are not artificially designed to favor any specific method,  but are directly inspired by real-world challenges that UAV agents routinely encounter during visual active tracking. This characteristic makes DAT more aligned with real-world scenarios compared to existing benchmarks. Therefore, experimental comparisons on DAT offer a more reliable and realistic evaluation of model performance under real-world constraints.\n* **Superiority of the GC-VAT method stems from innovative reward design, independent of new properties.** The proposed goal-centered reward (Section 4.2) is inherently general-purpose: it only requires the target’s 2D image-plane projection and does not rely on task-specific heuristics. This enables accurate target localization under varying viewpoints, making it applicable to a wide range of VAT settings. \n\n    As theoretically analyzed in Section 5, the goal-centered reward accurately reflects the target position in the image plane under varying viewpoints, providing consistent policy guidance. In contrast, existing methods assume fixed forward views and use distance-based rewards that capture Euclidean proximity but not visual alignment. When viewpoints change, these rewards fail to indicate whether the target is in view or properly framed. \n* **Our method significantly outperforms baseline methods even when new properties are removed**. We simplify the DAT scenarios to fixed sunny weather conditions with targets moving only along straight roads, and compare the within-scene and cross-scene performance of GC-VAT against baseline methods, with all methods trained on the simplified citystreet-day map. As shown in Table A, even when properties such as weather variations and complex dynamics are removed, the GC-VAT method continues to significantly outperform baseline methods. \n\nTable A. Comparison result on simplified citystreet-day map.\n|Metric: CR|Within-Scene|Cross-Scene|\n|-|-|-|\n|AOT|15±1|15±0|15±0|\n|D-VAT|13±1|13±0|13±0|\n|**Ours**|**266±76**|**148±71**|\n\n|Metric: TSR|Within-Scene|Cross-Scene|\n|-|-|-|\n|AOT|0.08±0.01|0.08±0.00|0.08±0.00|\n|D-VAT|0.07±0.01|0.07±0.00|0.07±0.00|\n|**Ours** |**0.82±0.14**|**0.52±0.23**|\n\n>Q2. The font of the texts in the figures are too small to read.\n\n**A2.** Thank you for your suggestion. We have incorporated the modifications in the revised version of our manuscript.\n\n>Q3. Since the proposed benchmark is pretty realistic and models many properties in the real world, it would be especially interesting if GC-VAT could be tested in the real world on real robots. \n\n**A3.** We appreciate the reviewer’s valuable concern regarding the real-world validation of our method. We are pleased to inform that **we have successfully conducted real-world drone deployment**. Specifically:\n\n* **Real-World Drone Deployment**. We deploy the GC-VAT policy on a DJI Mini 3 Pro drone to actively track a moving vehicle. We use DJI’s Mobile SDK to stream live video to a computer for policy inference and to send control actions back to the drone for real-time execution, forming a closed-loop system.\n* **Zero-Shot Sim-to-Real Deployment and Results.** GC-VAT demonstrates strong robustness without any fine-tuning, despite real-world challenges such as airflow, sensor noise, and communication latency. As shown in Table B, across **four test trajectories**, it achieves an **average zero-shot Tracking Success Rate of 88.4%** and an **average Correct Action Rate of 81.3%**, demonstrating its strong effectiveness in real-world conditions. \n\nWe acknowledge that visualizations of Sim2Real deployment would improve clarity. However, due to NeurIPS rebuttal policy, images and videos are not permitted at this stage. We will include  frames and clips in the revised manuscript.\n\nTable B. Results of real-world drone deployment. AOT [34] and D-VAT [17] fail to learn effective tracking policies even in simulation, and therefore cannot be successfully deployed on real drones.\n|Metric|Tracking Success Rate|Correct Action Rate|\n|-|-|-|\n|AOT|-|-|\n|D-VAT|-|-|\n|**Ours**|**88.4%±8.0%**|**81.3%±1.4%**|\n\n****\nWe sincerely hope our clarifications above have addressed your concerns. We would be grateful if you could kindly reconsider the evaluation of our paper."}]

Author rebuttal:

Meta-review: In the paper the authors present a novel benchmark designed for open-world drone active air-to-ground tracking. The benchmark features high-fidelity simulation environments and human-like target behaviors. This adress the current lack of unified benchmarks and diverse environmental conditions in the drone tracking domain. The authors also propose a goal-centered reward formulation that provides viewpoint-sensitive feedback to the agent.
Strenghts of the paper include (i) first open-world benchmark for drone active aerial-to-ground tracking, (ii) well written paper, (iii) interesting goal-centered reward formulation, (iv) Good improvement to cumulative reward metric, (v) rich variety of scenes and target types, (vi) solid analysis on the shortcomings of existing methods. 
Weaknesses include (i) limited experimental validation using 2 previous methods, (ii) 
Reviewers also suggested replacing the CNN backbone to a transformer based one and also provided other ideas of improving the paper. 
The authors provided a good rebuttal. Both reviewers and authors engaged in the discussion, after which the reviewers all suggest accepting the paper with grades 4,4,5,5. I recommend accepting the paper.
Decision: Accept (poster)