Title: Open-World Drone Active Tracking with Goal-Centered Rewards
Venue: NeurIPS 2025
Accepted: yes
Keywords: Drone Tracking Agent; Drone Active Tracking; Goal-Centered Reward Design; Open-World Tracking Benchmark; Cross-Domain Generalization in Drone Tracking
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength: \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.\n\nQUESTIONS:\nSee above\n\nLIMITATIONS:\nNo negative societal impact need to be discussed", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nAdvantages:\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.\n\nQUESTIONS:\n1. 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\n\nLIMITATIONS:\nNo", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nSee 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-12"}