Embodied Crowd Counting
Authors: Runling Long
Yunlong Wang
Jia Wan
Xiang Deng
Xinting Zhu
Weili Guan
Antoni B. Chan
Liqiang Nie
Venue: NeurIPS 2025 poster ()
Keywords: crowd counting
embodied AI
Primary area: reinforcement_learning

Abstract: Occlusion is one of the fundamental challenges in crowd counting. In the community, various data-driven approaches have been developed to address this issue, yet their effectiveness is limited. This is mainly because most existing crowd counting datasets on which the methods are trained are based on passive cameras, restricting their ability to fully sense the environment.
Recently, embodied navigation methods have shown significant potential in precise object detection in interactive scenes. These methods incorporate active camera settings, holding promise in addressing the fundamental issues in crowd counting. However, most existing methods are designed for indoor navigation, showing unknown performance in analyzing complex object distribution in large-scale scenes, such as crowds. Besides, most existing embodied navigation datasets are indoor scenes with limited scale and object quantity, preventing them from being introduced into dense crowd analysis. Based on this, a novel task, Embodied Crowd Counting (ECC), is proposed to count the number of persons in a large-scale scene actively. We then build up an interactive simulator, the Embodied Crowd Counting Dataset (ECCD), which enables large-scale scenes and large object quantities. A prior probability distribution approximating a realistic crowd distribution is introduced to generate crowds. Then, a zero-shot navigation method (ZECC) is proposed as a baseline. This method contains an MLLM-driven coarse-to-fine navigation mechanism, enabling active Z-axis exploration, and a normal-line-based crowd distribution analysis method for fine counting. Experimental results show that the proposed method achieves the best trade-off between counting accuracy and navigation cost. Code can be found at https://github.com/longrunling/ECC?.

Embodied Crowd Counting
Runling Long', Yunlong Wang's Jia Wan X Deng', Xingting Zhu", Weili Guan', Antoni B. Chan", Liqiang Nie
HHarbin Institute of Technology, Shenzhen
CCiy University of Hong Kong
(24b951033 220122200 hit. edu cn
{jiawan1999 [ {dengxiang Seaagkiianggguaneiiliiee.... guanweili)@hit. edu
com
{xt. zhu)@my cityu. edu hk
(abchan) Ocittu. edu. hk
Abstract
Occlusion is one of the fundamental challenges in crowd counting. In the commu- nity, various data-driven approaches have been developed to address this issue, yet their effectiveness is limited. This is mainly because most existing crowd counting datasets which the methods are trained are based passive cameras, restricting their ability fully sense the environment. Recently, embodied navigation methods have shown significant potential in precise object detection in interactive scenes. These methods incorporate active camera settings, holding promise in address- ing the fundamental issues in crowd counting. However, most existing methods are designed for indoor navigation, showing unknown performance in analyzing complex object distribution in large-scale scenes, such crowds. Besides, most existing embodied navigation datasets are indoor scenes with limited scale and object quantity, preventing them from being introduced into dense crowd analysis. Based on this, a novel task, Embodied Crowd Counting (ECC), is proposed to count the number of persons in large-scale scene actively. We then build up an interactive simulator, the Embodied Crowd Counting Dataset (ECCD), which en- ables large-scale scenes and large object quantities. A prior probability distribution approximating a realistic crowd distribution is introduced to generate crowds. Then, zero-shot navigation method (ZECC) is proposed as baseline. This method contains an MLLM-driven coarse-to-fing to-fine navigation mechanism, enabling active Z-axis exploration, and a normal--inn-based crowd distribution analysis method for fine counting. Experimental results show that the proposed method achieves the best trade-off between counting accuracy and navigation cost. Code can be found at httpps//gtaabcco//ogguunnn////c/
Introduction
Crowd counting is critical for public safety and urban planning 23 One main challenge in this field is occlusion. It can be categorized into two aspects: overlap and invisibility. Overlap refers to the high density of people stacked together, making it difficult to distinguish each individual from some viewpoints, while invisibility indicates that the current camera position is obstructed, such as being blocked by buildings, far from the crowds that the target cannot be detected clearly. To summarize, these situations are caused by a poor observation point. Existing methods try to solve these challenges from several perspectives, such using multi-scale feature extraction (22), body part detection [30), or multi calti-camera fusion [56] (57. Datasets are also expanded for enhancing method
"Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Robot Finegtiin Datrstt
Cread Countig Reteet
Good Courting Model:
O
NN
Meek!
200C
Agest
4083L un!
(a)
(b)
(c)
Figure I: (a) Comparison between ECCD and existing embodied navigation datasets. ECCD features
large-scale outdoor crowd scenes. (b) Comparison between ECCD and crowd counting datasets.
ECCD enables interactive ability. (c) Comparison between ZECC and existing crowd counting
methods. ZECC is an agentic framework with automatic camera adjusting ability.
Table 1: Comparison between ECCD and other related datasets. ECCD combines features from both
crowd counting datasets and embodied navigation datasets.
Dataser
Active camera
Place
Max target quantity ssample
Dynamic tirget
weo Instuction
Task
NWPU--Doo
10
200333
x
SenseCrowd
27
296
CC
DukeMTMC
R2K 01
Indoor (Ciround)
X
VLN
HM3DSem
Indoor (Ground)
X
ObjNav
ALN
City Aerial)
VLN
Cityaa
25
CitytArrial)
Opently
11
City Aerial)
X X
VLN
BCCD
CntyArriial
15,488
BCC
performance 58
50 In general, these methods or datasets either try to enhance learned model representation expand the camera sensing range by introducing multi-view settings. Yet, their passive camera settings do not fully solve the occlusion challenge crowd counting, especially in cases where crowds exceed the sensing range, or no cameras are set to detect completely obscured crowds. These issues restrict passive-cameraa based methods in practice.
42
Recent development in Embodied AI brings a new perspective to address the occlusion challenge in crowd counting. It has been demonstrated to possess significant potential in enhancing scene exploration and object detection. Aspects such as Laaon-Laaaagaaaaiigaittttt directed towards equipping mobile robots with human-like perception abilities, resulting in remarkable performance in exploring scenes and detecting objects in open environments (9, [5] 59 g. The active camera settings in VLN are promising to solve the fundamental challenges in crowd counting, since it can optimize observation points to mitigate overlap and invisibility caused by fixed camera position are indoor environments with limited exploration space (c.g., no Z-axis action options) and relatively small object quantity. And the performance of such methods remains unknown for detecting crowds with large quantity and complex distribution in large-scale scenes. This results in significant gap between VLN and crowd counting, in practice, crowds often appear in large spaces with varying distribution.
( (VLN) have been
settings. Yet most VLN benchmarks [51 46
To address the issues, we first define novel task, Embodied Crowd Counting (ECC), which is shown in Figure [ The task is defined as counting the total number of people present in a large outdoor scene using a drone. Given the absence of an existing dataset, we have created new dataset called the Embodied Crowd Counting Dataset (ECCD) specifically for this task. This dataset includes 60 unique and diverse large-scald scale outdoor scenes. Each scene spans area of up 40,000 m² and has a target crowd size of up to 15,000 individuals. To ensure realism in our dataset, we employ a Poisson Point Process [3] for modeling the distribution of crowd sizes, which effectively simulates real-world crowd scenarios. The comparision of ECCD and related datasets are shown in Table
In this study, we introduce baseline method, Zero-shot Embodied Crowd Counting (ZECC) aiming at counting individuals present in environments populated by crowds. Given the ability of interacting dynamically with surroundings using foundation models, modern argentic methods achieve even better performance against training-based methods while maintaining generalization ability. 9 24 12 31
Inspired by this, we aim to build a zero-shot baseline that can generate to diverse scenes leveraging foundation models, using an argentic paradigm without a vast amount training data. The primary challenge lies in choosing suitable navigation points to find balance between efficient exploration

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and effective detection. To address this, our approach consists of two main components: the Active Top-down Exploration (ATE) method and the Normal-line Based Navigation (NLBN) system. The ATE method serves as an efficient exploration strategy that employs coarse-to-fine navigation approach. It utilizes the common sense capabilities of Multi-Modal Large Language Models (MI.LM) to assess the environment, allowing for effective planning of vertical movement (Z-axis exploration). This takes advantage of the six degrees of freedom (6-DoF), which helps to avoid obstacles at lower altitudes and provides broader view of the surroundings, thereby facilitating better exploration. Following the rough estimation of crowd distribution produced by ATE, we propose the NLBN to create precise navigation points for improved crowd detection. By using the normal lines of surfaces, we can establish detailed observation points that achieve a balanced trade-off between exploration efficiency and detection performance. This method addresses challenges related to overlapping individuals and visibility issues, ultimately leading to more accurate crowd counting. Experimental results show that the method is effective due its interactive ability. The contributions of this work can be summarized as follows:
We present an innovative task called ECC, specifically designed to address the challenges of occlusion and multi-scale complexities that are prevalent in conventional crowd counting methods.
A new dataset called ECCD has been collected to redefine the landscape of crowd analysis. Unlike traditional crowd counting and VLN datasets, it features large-scale outdoor crowd scenes with interactive capabilities.
We propose a baseline method, ZECC, using MLLM for Z-axis exploration, reducing costs while ensuring detection performance. By utilizing normal lines to calculate navigation points, this approach eliminates occlusion and enhances visibility in crowds.
2
Related works
2.1 Crowd Counting
Crowd-counting algorithms have greatly benefited from large-scale, high-quality datasets like UCF-
CC50 (20 and UCF-QNRF [21] These foundational datasets have facilitated the creation of subsequent collections focused on dense crowd imagery, such as ShanghaiTech [58]. JHU-CROWD++
42). NWPU-Crowe [50]. However, the images in these datasets are generated from fixed cameras.
In contrast, ECCD provides interactive capabilities while maintaining diverse crowd distribution.
leverage crowd distribution prior in an image, or use attention maps to leverage rision-language models to transfer image-text knowledge to dense crowd prediction. While these models improve long-range and overlapping small target detection ability, their performance is restricted if the overlap reaches an extreme level. Recent efforts expand spatial coverage by multi-view systems [37 19] that use multiple cameras to capture images from large-scat scale scenes. Others like [17 16) use recorded video to conduct crowd analysis. Although these advances represent significant progress in addressing the basic challenges of crowd counting compared to traditional methods that rely on fixed images, the camera settings remain predetermined by the dataset. This restriction means that the settings cannot be modified during the inference process, limiting the ability to fully examine larger environments. ZECC allows active exploration, which is fundamentally different from existing methods.
Methods like (58 22
learn dependency. Recent multi-modaa approaches [10, 54 33 48
2.2 Embodied Navigation
Many traditional robot navigation methods were developed using conventional navigation datasets like KITTI [14] and SUN RGB-D (44). Beyond these foundational datasets, [51 38) provide 3D indoor environments for navigation and interaction tasks. These datasets are limited with their small scale size and small object quantity. Recent datasets have been created towards outdoor navigation
11 28 However, they are designed for VLN tasks without consideration of object quantity. Compared to these datasets, ECCD simultancously supports large-scale outdoor scenes and large object quantity with diverse distribution.
22
25|
Efficient exploration using mobile robots remains a crucial challenge in vision and robotics. 15
18
22 have developed human-like cognitive maps, enabling autonomous path learning in unknown environments. Other approaches like [3] 7 36) use reinforcement learning to develop
35
3

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applied video-based visual- -anuua-l models to plan sequential actions in VLN. And [3] 60 presented zero-shot models that utilize natural language instructions to guide agents through environments without prior unvironment-speciinn
exploration policies. Recently, 55
training. However, the methods mentioned above are for indoor navigation without Z-axis moving ability. This restricts their ability in large-scale outdoor scenes. Recently, methods like (29 52 introduce 6-DoF in outdoor VLN. Yet they are designed for instruction following tasks, in which the movement restricted by human language. Besides, they lack the ability to analyze crowds with a large quantity and complex distribution. In contrast, ZECC the first self-motivated agent in 6-DoF that can handle crowds.
3 Method
3.1 Problem Definition
To ensure the interactive with environment for accurately crowd counting in vast outdoor environ- ments, we propose an innovative task called Embodied Crowd Counting (ECC). In ECC, an agent is first deployed in unknown environment. At time step t, the input of the agent is RGB-D observa- tions O₂ along with its pose pt. Based on this information, the agent predicts navigation point C R to drone at time step f and the drone moves to p. During exploration, the agent is allowed to record observations that help with crowd counting. When the agent decides to stop, it outputs an integer that represents its crowd counting result. The counting error and travel distance the agent's performance. ECC can be considered analogous to the Zero-Shot Object-Good Goal Navigation (ZSON) task [32], since they both require agent to detect targets in an unexplored environment without additional assistance. However, ECC faces unique challenges: 1) The Z-axis is available, which is not considered by most ZSON methods. 2) Complex crowd distributions exist, including heavy occlusions, while ZSON methods only consider fewer objects. Current ZSON methods show unknown performance under such differences, and new methods should be considered in ECC. Note that ECC is different from the instruction following tasks in VLN such as 22 52], since these tasks require natural language assistance drive the agent.
calculated to assess
3.2 Embodied Crowd Counting Dataset (ECCD)
We propose new dataset called ECCD to support algorithm design and evaluation for the community, developed using Unreal Engine 4. This platform enables programmable environment construction, allowing scene richness and scalability. The characteristics of ECCD are as follows:
Diversity. Since different environments have different layouts, crowd distributions, and quantities reflect different challenges, ECCD is designed to contain diverse scenes. This dataset contains 60 distinct environments. Also, ECCD has an area up to 40,000 m²? reaches height up to 50 m, and allows for the simultaneous presence of over 15,000 targets within a single scene. This is significantly different from existing robot navigation datasets, such (38), which offers environments with an average navigable space of 1,000 m and crowd counting datasets, such as (58), which contains 1,198 annotated images captured with static cameras and a total of 330,165 individuals.
Realism. The ECCD designed to simulate large-scale outdoor crowd scenarios in reality, order to ensure practical effectiveness of systems built on ECCD, as shown in Figure (a). Environments like a city, a stadium, and parking lot are simulated. Additionally, the environments support a complex structure of buildings in real life, such as multiple floors and bleachers. These features allow ECCD to reflect challenges in reality.
Crowd generation mechanism. To model real distribution in crowd counting, ECCD uses Poisson Point Process for crowd quantity distribution modeling [3]. In ECCD, Blocks are placed in potential areas where crowds may exist. Then, for each block l4 C IR,, the process is defined as:
TP (NN = -b) (A U e 1-1 k E
k!
where N()) denotes the number of individuals in block 24, represents the crowd density set by human experts according to the environment semantics, and [4/] is the area of the block. This ensures ECCD generates crowds that approximate real situations. By comparison, existing simulators based on UE4, such as AiN and OpenUAV, do not consider object quantity and distribution.

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ATL ventte
(a)
Figure 2: (a) ECCD designed mimic building and crowd distribution realistically. On the left are samples from ECCD, and on the right are the real scenes. (b) Illustration of the potential navigation vectors, normal lines, and FBE view vectors. Zoom in for better visualization.
Active Top down Exploration
Normalline Based Navigation
Fine Dotection and Counting
Enviromment
AlL
T8T Iec
Kmah
Detector
Denily
Sisencttio
Figure 3: The proposed framework. First, ATE is proposed to estimate the global crowd distribution efficiently. Then, NLBN is proposed to generate fine observation points, alleviating crowd overlap. The final result is generated by aggregating all fine detections.
3.3 Zero-shot Embodied Crowd Counting (ZECC)
3.3.1
Overview
Previous embodied navigation agents are designed for indoor environments 311 or relying on language assistance (29 (2], making them restricted in large-scale outdoor scenes. Under such a context, zero-shot agent, ZECC, which can actively control altitude and conduct crowd analysis, is proposed. As illustrated Figure 3 the method consists of three components: Active Top-down Exploration (ATE), Normal-line based Navigation (NLBN), and Fine detection. ATE is designed for adjusting agent altitude for efficient coarse crowd distribution estimation, and NLBN estimates normal lines top of the crowd for accurate crowd observation, alleviating occlusion.
3.3.2 Active Top-down Exploration (ATE)
Crowds are often concentrated in specific areas, such as roads and squares, making it unnecessary to explore every region of the environment. To address this, ATE is proposed to estimate global crowd distribution by changing altitude. This approach aims to improve efficiency by focusing exploration efforts on the most relevant areas. particular, high-altitude exploration (HAE) brings a broader field of view for decision making, well relatively less exploration cost since obstacles are often sparse high altitude. In contrast, low-altitude exploration (LAA) provides close-range observation for precise target detection. The agent needs to plan HAE and LAE to achieve efficiency and accuracy simultaneously. Therefore, ATE leverages the Z-axis mobility of outdoor agents and the common sense of MLLM switch between HAE and LAE using local environment layout reasoning. Then, the crowd distribution is estimated by crowd counting model to predict density maps.
Specifically, the agent collects observations O {of..... of) and pose pt at time step 4, where indicates the camera number of the agent. Then, an MILM prompted using observations and text prompts to conduct environment layout reasoning. The MLLM is asked predict whether the current location is valuable for LAE, by referring to the current crowd appearance and obstacle layout. This process is formulated
St MLLM (0,1),
(2)
5

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where MLLM (-) is the inference process and is the prompt. E [0, 1]. If st > 0.5, the agent will adjust its altitude for LAE. After switching the altitude strategy, the agent will conduct regular exploration. For HAE, one of the frontiers between explored and unknown areas is selected as navigation point. For LAE, the agent keeps exploring until areas within its field of view during HAE are fully explored. During LAE, once the agent reaches navigation site f, it gathers observations
O, o0....00 .f)
}.
and a crowd counting model predicts crowd density maps on the observations.
Then, the density maps are projected onto the global point cloud form global crowd distribution:
dy P (Or) .P))
(3)
where P P() is the projection operation, G is crowd counting model, and py is the agent pose at f.
3.3.3 Normal-line based Navigation (NLBN)
NLBN is designed to analyze overlapping structures in dense crowds by converting the crowd detection task into surface detection, which helps identify optimal observation points. While random viewpoints may impair visibility, vantage points located above the center of the crowd provide clearer views for individual identification. This top-down perspective enables the distinction of overlapping individuals and maintains targets within the field of view (FOV). Initially, NLBN clusters the crowd point cloud into subregions using a clustering method, simplifying the analysis by breaking the large point cloud into manageable parts, as navigating to each point can be resource-intensive. Surfaces are then fitted derive normal lines, thus transforming complex crowd analysis into more straightforward surface analysis. Finally, optimized navigation points are generated based on these normal lines, ensuring they elevated to facilitate accurate crowd detection while employing viewpoint-basee approach to avoid overlaps with obstacles.
In particular, GMM 41 is used to divide the global crowd distribution into manageable subregions. can divide any crowd distribution into patches. parameter e used to determine the size of each GMM cluster. Surfaces are fitted for each patch afterwards. Then, for the N-th cluster, the normal obtained and represented by deluuster, Candidate view directions view d vieww N are sampled under angular constraints:
delasteer
d view N
C,
(4)
[dclsteer
view
||
where ( is a hyper parameter. These candidate view directions can bring the agent to optimized observation points by selecting a position along the vector. However, there are two issues: 1) The position may be located on obstacles; 2) The crowd cluster may be out of the agent's FOV. Since the propagation of light naturally points to unobstructed areas, the ATE viewpoints are used to generate the final navigation points, which are calculated
ATE
ATE
cluster
(5)
x where x is the navigation point from which the agent finds the cluster center x xlluster during ATE. Then, the potential navigation directions that are at the minimum angles to the ATE view vectors selected as the final navigation directions:
AAEE
view
d view arg min m
NE
(6)
Nm
and the final navigation point xN is calculated by
view
x
cluster + 17 view
(7)
where 17 is a hyper parameter representing the distance between the agent and the crowd cluster. NLBN ensures close-range range, precise target observation and safe navigation, even in complex and unfamiliar environments. The potential navigation vectors, normal lines, and ATE view vectors are illustrated in Figure
3.3.4 Fine Detection and Counting (FDC)
Using the navigation points generated by NBLN, the agent travels from one point to another through path planning algorithms. Close-range and high-resolution RGB observations can be conducted upon

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Table 2: Comparison with ZSON methods. TaCle 3: Comparison with MVC methods. ZBCC
achieves à trade-off between MAPE and TD.
achieves trade-off between MAPE and cost.
Method
MAPE (%)
(m)
Metbod
MAPE (8)
#ol Cameras
FBE
451
+ GL
47
57.19 1.83
2513.06 = 247.35
MVF-10 56
15.13 # 0.00
1735.32 = 0.00
FBE
+GD
39
53,38 1.26
2513.06 247..35
MVF-20
56
39.92 0.00
7
CoW
9
+G
47
52.75 à 1.52
3449.51
1: 127.2
MVF-30
556
61.43 a: 0.00
333.32 0.00
9
46.01
3449.51
127.2
CounfFormer
1735.32 0.00
Opy
+ GL
F7
60.57
2.43
5069.64
183.23
CountFormer-20
34
36.26 + 0.00
747.32 0.00
OpenFMNNv
GD 39
49.41 = 2.35
5069.64 = 183-23
CountFormer..
56.76 0.00
313.32
ZFCC
3722.45 + 73.78
ZECC
18.71 a 1.41
0.000
reaching each navigation point. These observations then utilized for detection models to perform precise target detection. The detection results are projected onto the global point cloud using the depth sensor and the agent's pose. To prevent repetitive detections, only target is retained for each region within a specific scale. Ultimately, the result is calculated by counting the number of filtered targets.
Experiments
Baselines. We compare ZECC with exploration methods: Frontier-based exploration (FBE) 45
ZSON methods: CoW [9] and OpenFMNav (24), and multi-view counting (MVC) methods [56 34
Metrics. Mean Absolute Percentage Error (MAPE) is used evaluate the counting performance: x 100% where M is the quantity of testing environments, D: and yı the estimated count and the ground truth count, respectively.
MAPE 37 E
For ZSON methods, the sum of Euclidean distance between adjacent navigation points along the agent traveling path is used to evaluate the travel distance (TD), which defined as: TD
IMM
|x+1- x1l,, where n is the quantity of navigation points one episode, x; and x,-1 are the
coordinates of the two adjacent navigation points, respectively. For the multi-view crowd counting method, we report the number of cameras required to achieve comparable performance to the proposed approach.
is used as path planning method. Altitude is 80m for HAE and 10m for LAE. The crowd density estimator in ATE is Generalized Loss (GL) [47], and the detection model in FDC is Grounding DINO (GD) 39 For hyper parameters, navigation vector deg Ç is 15 9 density threshold x is 0.7, navigation point range is 8 m, and cluster size 6 40. For the ZSON methods, the exploration step limitation is removed. When the methods finish an exploration step, they additionally get RGB-D captures of the environment. As exploration stops, they are equipped with GD or GL conduct crowd counting using the projection method in FDC upon their captured images. For MVC, the scenes are divided into grids with diverse intervals, which are 10m, 20m, and 30m. Four cameras at poses of 09, 90", 180%, and 270 are placed on each grid intersection to obtain RGB captures. Then the captured images are sent to the MVC to get counting results. All methods are proceeded on a platform with IntelCorei9- 14900KF, 128GBRAM, and NVIDIA GeForce RTX 4090 GPU.
Implementation Details. MILM used in ATE GPT-4V 53
Results
5.1 Overall Performance
Comparison with ZSON methods. We report performance in Table 2 ZECC offers the optimal balance between counting performance and cost. In contrast, FBE delivers the best TD but cannot perform target detection function, which limits its capacity for fine observation. ZSON methods come with perception modules; however, they do not select a 3D fine observation point as NLBN, which means they cannot fully observe each individual in the crowd.
Agents utilizing counting models for crowd counting tend to perform slightly lower than agents using detection models. This is due to the increased sparsity of crowd observations as the agents get closer to the crowd. Crowd counting models are mainly trained on images depicting dense crowds, which results in limited generalization capability for scenarios with sparser crowds.
7

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Average performance different methods
Average cost of different methods
6000
60
MAPE (9)
TD (m)
CoW
40
4000
DFM
ZECC
20
HM
2000
Figure 4: Performance and cost of ZECC and the baselines under different crowd density levels.
LI-L5 refers to increasing density level. The figure demonstrates that ZECC achieves a balance between performance and exploration cost.
Table 4:
The ablation study for ATE. ZECC achieves better trade-off between performance and exploration efficiency (reducing 17% cost with
Table 5: The results of the ablation study for com-
ponents in NLBN. ZECC achieves the best perfor-
mance and success rate.
8% performance decline).
Method
MAPE (%)
Successful rate (%)
Method MAPR (%)
TD (m)
wo NLBN
65.19
100.00
w/o NI.
98.44
8.33
w/o HAE
17.46
4633.67
w/o VPS
92.55
100.00
w/o LAE
88.08
1738.84
w/o ATE-VPS
99.49
1.45
ZECC
18.91
3804.63
ZECC
18.91
100.00
We analyze various environments with different crowd density levels and visualize the average perfor- mance and cost of the methods using the GID detector in these scenarios. The results are presented in Figure 4 Among the methods evaluated, ZECC demonstrates the best average performance across different density levels. As crowd density increases, the MLLM in ATE with NLBN becomes more effective at identifying and observing high-density crowds, resulting in improved performance. In contrast, other methods struggle to detect high-density crowds.
In terms of cost, ZECC's navigation points influenced by crowd distribution and density. As the density level increases, the cost also rises. Although ZECČ falls short of achieving the lowest cost in the last two density levels, its costs still comparable the baseline while ensuring effective counting performance. In contrast, other methods do not actively adjust navigation points. Their costs remain relatively stable, yet their performance is limited.
Comparison with MVC. The comparison with MVC is shown in Table 3 " 1-10" refers to MVC methoods using a grid interval of 10m. CountFormer- 10 achieves the best performance, while ZECC still provides a close performance by reducing the camera used significantly. This illustrates the advantage of the active method over the multi-view method.
5.2 Ablation Study
ATE. We conducted ablation studies by removing specific components from ATE. The results are presented in Table w/o HTE refers to using FBE NLBN results for crowd counting. w/o LAE refers to fix the agent's 's altitude to HAE. w/o HAE performs best by exploring environments greedily. but results in a higher TD. ZECC achieves a better balance by conducting both HAE and LAE simultaneously. To further illustrate this trade-off, we conducted an experiment by fixing TD for FBE in w/o ATE and ATE in ZECC. Once the agent reaches TD threshold, will conduct NLBN using partial estimated crowd distribution. The result is shown in Figure 5 (a). ZECC demonstrates better performance with less cost when TD is limited in most cases, illustrating that ZECC can efficiently estimates global crowd distribution. On the other hand, w/o ATE is not effective when TD is limited.
NLBN. We then conducted ablation studies on NLBN and the results are presented in Table 5 w/o NLBN refers to using ATE results for crowd counting. w/o NI. refers to not using normal line (NL. to calculate navigation points but use the cluster centers as navigation points. w/o VPS refers to not using view point selection (VPS) but select a point along the normal vector with 7). w/o ATE-VPS refers to not using the NETE-viewwveetor-baseeee view point selection (ATE-VPS), but randomly select navigation vector from the potential navigation vectors. Successful rate indicates the ratio of the reachable navigation point reported by path planning algorithm. The findings indicate that omitting any component of NLBN results in a significant drop in either performance or the success rate. Without NLBN, the absence of optimized viewpoints causes ZECC to revert to a ZSON method. When both NL and TBE-VPS are removed, most navigation points end up being located on obstacles. Additionally, without VPS, the navigation points are positioned directly above the targets, causing the targets to fall out of the field of view.
8

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Cluster size Ve1
Navigation Wector Degres (5)
Performance under fixed TD
0.6
MAPE (56
WPb ATF
0.4
Nevigation Point Renge Inl
Density Map Threshold (x)
0.2
NorT (3)
26
thresho di (m)
(a)
(b) [b)a2
Figure 5: (a) Comparison of performance-com trade-off. ZECC achieves better trade-off when TD
is limited. (b) The effect of four hyper parameters in ZECC. It shows that ZECC is effecive when the hyper-parameters are set in reasonable scopes.
(b)
(c)
Figure 6: Navigation point (green) and trajectory (red) of different methods. Bleak dots are ground- truth. (a) ZECC. (b) OpenFMNAV. (c) MVC. ZECC shows less exploration non-crowded areas while setting navigation points actively based on crowd distribution. Zoom in for a better view.
5.3 Hyper parameter Study
The influence hyper parameters is studied, which include cluster size, navigation vector degree, navigation point range, and density map threshold. A gym-like scene featuring densely packed crowd is utilized to test these parameters. The results are illustrated in Figure (b).
Cluster size. Larger generates fewer observation with coarser detection, while low generates more navigation points and reduces efficiency (TD 5125.44m for 30, 4065.02m for e = 40).
Navigation vector degree. The agent has a limited field of view for large Ç and obstructed for low This highlights the importance of the NLBN since the method generates robust navigation points for different scenes.
Navigation point range. At close range, the agent's field of view is restricted, and at long range, the agent is unable to gather detailed observations, which negatively impacts the subsequent target detection phase.
Density map threshold. lower density map threshold leads to an expansion of the target area, resulting in an increased number of navigation points. However, this also raises the associated costs. For instance, when x = 0.5, TD 6408.02 m, increasing to 7272.02 m when к 0.4. This is significant degradation in efficiency, yet the improvement in performance is not significant.
Generally, the influence hyper parameter consist with tuition. ZECC provides effective perfor- mance if they are not set to extreme value.
5.4 Case Study
We qualitatively illustrate how ZECC can alleviate occlusion and overlap while improving efficiency by comparing trajectory in two scenes with OpenFMNav and MVC-30. The results shown in Figure 6 It shows that ZECC can effectively explore a complex occlusion environment, reduce exploration in low crowd density areas, and navigation points based crowd distribution. These features result in trade-off between performance and cost.
6 Conclusion
In this study, propose task that enables interactive crowd counting: Embodied Crowd Counting (ECC). simulator, the Embodied Crowd Counting Dataset (ECCD), developed enable related research for ECC. This dataset includes 60 diverse virtual environments with crowd density modeled

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by prior probability distribution, approximating reality. A method, Zero-shot Embodied Counting (ZECC), is proposed to verify this task. This an active agent that can explore unknown environments without additional assistance. Active Top-down Exploration (ATE) is proposed to utilize 7-axis moving ability for exploration planning. This module is equipped with MILMM to enable active high altitude exploration (HAB) or low altitude exploration (LAE), balancing crowd counting performance and exploration cost. Normal-line based Navigation (NLBN) is proposed to select an optimized navigation point for crowd observation. This module generates navigation point from the top-down view and maintains an angle, alleviating the overlap the crowd. Simultaneously, the estimated navigation points enable obstacle avoidance and ensure that the crowd is in FOV. Experiment results show that ZECC achieves a balance between performance and cost compared to recent navigation agents. As the first work to propose ECC, we leave expending ECCD and ZECC to dynamic targets and real world application to future work, which are not considered by existing methods.
7 Acknowledgment
This work was supported by the Science and Technology Major Project of Jiangsu Province No.BGG202401), This work was supported by the National Natural Science Foundation of China under Project 62406090. The work described in this paper was conducted in part by Dr ZHU Xinting. Jockey Club Global STEM Post-doctoral Fellow supported by The Hong Kong Jockey Club Charities Trust.
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16. Declaration of LLM usage
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Appendix
A Broader Impacts
Further research and careful consideration necessary when utilizing this technology, the presented proposed method relies on the simulator, which may possess biases and could potentially result in negative societal impacts.
B Visualization of ECCD
Visualization of several scenes in ECCD is shown in Figure T Figure ECCD offers diverse range of scenarios, encompassing both large-scale outdoor settings and indoor environments. It provides a hierarchical 3D structure, with crowds distributed across various positions within the 3D space, posing challenges for algorithms.
Figure 7: Visualization of environments sampled from ECCD.
Figure 8: 3D structure of samples from ECCD. Colorful dots represent crowd ground truth. Different colors represent different heights.
22

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C
Details of ZECC
C.1
ATE
During HAE, the ATE receives RGB images captured from the bottom and surrounding areas of the agent. These images are organized into panoramic view and transmitted to an MLLM in the form of prompt. The image prompt and the text prompt are shown in Figure The prompt utilizes the Chain of Thought mechanism to improve MLLM perception ability. Once the agent decides to switch LAE, will select spare space directly under its horizontal location and conduct landing operation. The pseudo-code is shown in Algorithm
Algorithm Pseudo-Code of ATE
Require: Global distribution D, HAB map MH. LAE map Mr, Prompt I
Ensure:
t 0:
2: donel {- False;
3: +- None
4: Mи + None
5: Mr. + None
6: while not donel do
7: Or, Ps t getState ():
8:
8t f MLLM (O,; I);
9:
Мμ +- updateMap (Oup:);
if 0.5 then
11:
toLAB();
12:
done2 &- False;
13:
while not done2 do
ps < getState();
15:
d - P (G (O )r):
Mr Map p;p));
17:
D - updateDistrrbuttion D dy);
done2 toUnexplored
19:
end while
toHAE0:
21:
end if
donel toUnexplored (:));
23:
t +-++;
24:
end while
25: return D
C.2 NLBN
The method used to estimate the normal line of the crowd cluster plane is the Open3D package. The global crowd distribution first divided into subareas using GMM cluster. Then, on each cluster, Open3D is used to estimate the normal line for each cluster. The normal lines are aligned with the vector (0.0,1) Then, the normal line at the cluster center is selected as normal line of the cluster. Based on this representation, the navigation points can be obtained. The pseudo-code is shown in Algorithm
C.3 FDC
During FDC, center each detection box can be projected to the global crowd distribution by using the depth information. To re-identify targets, the space is divided into 3D voxels with size of 0.25 m. All the target with in voxel regared as the same target. This configuration is for ZECC and all comparison methods.
23

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Algorithm Pseudo-Code of NLBN
Require: Cluster size Navigation vector degree Ç, Navigation point range 1), Density map threshold
K. Global distribution D
Ensure:
D t ATB(D);
2: {xeluster 1.... XN
xluuster Cluster(D);
3: done t- Folse;
4: for i = to N do
5: d deluster + (itPlane(x;; (x;luster);
6: ATE +- gerViewpoint D):
7:
Compute {d ..... viveww J: using Eq. 4
8: Compute d A using Eq.
9: Compute d diiww using Eq.
Compute x
xlew using Eq.
11: end for
12: while not done do
13:
done + -tNexxNaavPPinn((" viee ..... XN })
14: end while
As an intelligent agent, you will control a drone to detect crowds in an environment.
You will receive panoramic view "Inage:" about your current location.
image
shows the environment around the drone. You need to output a float in range [e, 1], indicating the score of whether current area valuable for crowd The higher score,
people detection. crowd or people existing this
higher the probability area. You need to particularis focus on the top-down inage, which is Direction 3. This image indicates your surroundings.
You need to think about what is in the image, analyze the environment layout, and analyze whether crowds exist in this area. Note that if crowds are absolutely not in the image, the should very 1ow. Output your thought in "Thoughts:" Then output your answer "Answer:".
For examples:
Image:
Thoughts:
The image shows an open area with obstacles, and there are absolutely no crowds.
Answer: 0.1
Image:
Thoughts:
The image shows area with heavy obstacles, although crowds or people cannot be clearly seen, under obstacles may exist crowds.
Answer: 9.4
Image:
Thoughts:
The image shows area with clear people crowds.
Now start answer
Image:
Direction
Direction
Direction 2
Mrackon
Direction 4
Figure 9: Prompt template used in ATE.
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D Occlusion analysis on ZECC
In this section, further test the ability of ZECC alleviate occlusion in the complex environments. For each ground truth person, we first select the navigation point which has the minimal Euclidean distance and then test whether occlusion occurs between the navigation point and the person. This is implemented by first projecting the global point cloud to the vector from navigation point to the person. Then, if the minimal distance between the global point cloud and the projected points is lower than threshold, the person is obstructed. To formulate this, the unit vector from person to its nearest navigation point is calculated as:
Pnavi perowd
navi
(8)
pnavi
Prrowd
where pnavi is coordinate of the navigation point and Perowd is the coordinate of the person. The vector from a global point cloud to the person
global
Pnavi
pglobal
(9)
where pslobal a random coordinate from global point cloud. The projected length is:
lij global
navi
(10)
and the projected point is:
Proj perowd big * v
havi s.t. by 2 0A44 S
Vij
global
(11)
The condition of determining the person is obstructed is: paiooall
min
p
A
(12)
In the experiment, À to 0.5 We compare ZECC with OpenFMNav and MVC-30. The ratio between the number of obstructed person and the number of ground truth person of the three methods shown Table It shows that ZECC suffers from the less occlusion, which benefits the crowd counting results.
E
Failure case study
ZECC fails when MLLM makes wrong decision. This is mainly due to occlusion by buildings. Figure I0 shows an example of MLLM planning failure, where the agent does not choose to conduct LAE at a crowd area.
Directon
Thoughts:
the image shows Lop-down view of reoftop area some structures
parking cars. There are no vistble people on crowds in the image. enviromeent appears to nostly eapty, with no signdlicant signs of huran activity.
Answer: 8.2
(b)
(c)
Figure 10: A case of ZECC failure. (a) Agent trajectory, (b) Top-down RGB. (c) MLLM reasoning. Blue box: ground truth crowd. Because of occlusion, MLLM failed to perceive the crowd during HAE.
F Quantitative results in real environment
To test ZBCC real environment, we use two outdoor crowd scene to verify the proposed method. The drone model used is the Taobotics Q300. The captured images are transmitted back to ground server using a remote communication protocol and fed into VGGT [9] to estimate relative point
clouds and poses. The proposed NLBN is then employed to calculate relative navigation points. The
25

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Table 6: Obstructed person ratio.
Table 7: Performance of real scenarios.
Method
Ratio (%)
Methodss
MAPE (%)
Secnario
Scenario
OpenFMNav 42.81
w/o NLBN + GL.
41.41
$7.76
MVC-30
21.77
NLBN GD
73.74
78.44
NLBN GL
22.50
29.31
ZECC
4.52
NLBN GD
15.17
19.83
drone's 's absolute geographic coordinates and poses are recorded in real-time by its GPS. By leveraging the relationships between absolute and relative geographic coordinates and poses, the navigation points calculated by NLBN are mapped to absolute navigation points. After the ground server controls the drone fly these absolute navigation points, it captures images of the crowd. We utilize Grounding DINO and the crowd counting model Generalized Loss to perform crowd detection or counting on images captured from both distant and close-up perspectives, respectively. The ground truth for crowd annotations is manually labeled. The visualization of the drone observation before and after adjusting its position to the NLBN navigation point shown in Figure [1 The quantitative results are shown in Table 7 Before applying NLBN, the drone can not obtain a clear view of the crowds in the target area. The GL estimation result is fuzzy, and due crowd overlap. people in the back rows are obstructed by those in the front rows. This results in the corrupted detection of GD. By adjusting to the NLBN navigation point, the drone is able to observe the target area from top-down perspective, alleviating occlusion of people in back rows and improving counting performance significantly. This demonstrates how NLBN addresses occlusion.
(b)
(c)
(d)
(
Figure 11: Quantitative results in real environment. (a), (d) Output of GL. The marked areas indicate the target zone. (b), (e) Point cloud of the scene. Red lines: candidate view vector. Green lines: the drone view vector. Orange lines: selected view vector. (c), (f) GD detection results. Red boxes: detected crowds. Green boxes: ground truth crowds. First row: before applying NLBN. Second row: after applying NLBN, (a), (b) and (c) are Scenario and (d), (e) and (f) are Scenario 2. By adjusting the camera position to the NLBN navigation point, the target detection result is improved.
26


Reviews:
[{"review_id": "KTVNGetJGW", "reviewer": "Reviewer_RciT", "summary": "In this paper, the authors propose a novel method for crowd counting using embodied agents that actively navigate environments to overcome occlusion and limited visibility—key challenges for traditional single-frame approaches. They introduce ZECC, a zero-shot agent guided by a vision-language model for effective exploration, and a geometry-based strategy for selecting optimal viewpoints for counting. For the novel Embodied Crowd Counting task, they build ECCD, a large-scale interactive dataset with realistic outdoor and variable crowd scenes. Benchmarking against other baselines, ZECC achieves better accuracy, demonstrating the effectiveness of active, embodied exploration for crowd analysis.", "questions": "* Can the authors provide insights or preliminary evidence on real-world applicability?  Given that the entire pipeline is evaluated in simulation, it remains unclear how well ZECC would transfer to real environments. Could the authors either discuss the expected challenges in real-world deployment or share preliminary qualitative results on real data? Can ECCD be enriched with real scenes, or made more realistic using existing sim-to-real approaches?\n\n* How critical is the vision-language model (GPT-4V) to the success of ZECC?  GPT-4V plays a key role in altitude decision-making during exploration, but it is a large, closed model. Could the authors clarify how much performance depends on this specific choice, and whether smaller or open-source alternatives (e.g., BLIP-2, MiniGPT) could be used effectively?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n\n* The paper introduces Embodied Crowd Counting (ECC), which frames crowd counting as an active exploration task using embodied agents. This formulation is more aligned with real-world scenarios (e.g., drones or mobile robots) than traditional static image-based approaches, which often fail under occlusion or poor viewpoints.\n\n* The authors release ECCD, a large-scale synthetic dataset that includes diverse and realistic outdoor environments with varying crowd densities. The dataset enables reproducible benchmarking and is a valuable contribution for the research community exploring active perception and crowd analysis.\n\n* The authors propose an effective method ZECC for the ECC task, which significantly outperforms other baselines across both accuracy and efficiency metrics.\n\nWeaknesses:\n\n* The paper's method and experiments are entirely conducted in a synthetic environment (ECCD), without any real-world evaluation. While the simulator is well-designed, transferring performance gains from simulation to real-world crowd scenarios is non-trivial due to domain gaps in appearance, noise, and agent behavior. This limits the practical significance and generalizability of the results, and applying the method to real data would likely require substantial adaptation.\n\n* The method is primarily a composition of existing components—such as pretrained vision-language models for navigation, classical path planning (A*), and standard crowd detection and clustering techniques. While the integration is thoughtful and effective for the proposed task, the paper introduces limited methodological novelty in terms of new algorithms. The strength lies more in system-level design than in technical innovation at the model level.", "was_revised": true, "final_justification": "I want to thank the authors for the provided rebuttal. The answers to my questions are clear, but I would like to maintain my original rating. The authors agreed that the method \"mainly focuses on system-level innovation\", which is an important contribution by itself (also considering the ECCD environment), and I reflect this in my positive original rating. However, methodologically, it undermines the novelty of the proposed pipeline, which is mostly a combination of existing algorithms and methods.\nRegarding the real-world evaluations, as the authors noted, 3DGS-based solutions or simulators could have been considered, as they are among the popular simulation environments used in the industry for such (closed-loop) evaluations. This would have made the paper significantly more solid.", "rebuttal": "> Q1: The method and experiments are entirely conducted in a synthetic environment (ECCD), without any real-world evaluation.\n\n A1: Thank you for your review to improve our work. We have provided a real-world application case in &zwnj;**Appendix 6**&zwnj; to test ZECC's capability in reducing occlusions in densely crowded scenarios. Besides, we provide results in another real-world scenario, which is denser compared to the existing one. The drone model used is the Taobotics Q300. The captured images are transmitted back to a ground server using a remote communication protocol and fed into VGGT [1] to estimate relative point clouds and poses. The proposed NLBN is then employed to calculate relative navigation points. The drone's absolute geographic coordinates and poses are recorded in real-time by its GPS. By leveraging the relationships between absolute and relative geographic coordinates and poses, the navigation points calculated by NLBN are mapped to absolute navigation points. After the ground server controls the drone to fly to these absolute navigation points, it captures images of the crowd. We utilize Grounding DINO and the crowd counting model Generalized Loss to perform crowd detection or counting on images captured from both distant and close-up perspectives, respectively. The ground truth for crowd annotations is manually labeled.\n\nThe results in Table 1 demonstrate that the methods with NLBN perform significantly better than baselines, which illustrates the real-world generalization ability of our method. We will add these results to the revised paper.\n\nTable 1: Performance before and after applying NLBN in the real scenes.\n\n| Methods | MAPE (%) in Scenario 1 | MAPE (%) in Scenario 2 |\n| --- | --- | --- |\n| w/o NLBN + GL  | 41.41 | 57.76 |\n| w/o NLBN + GD  | 73.74 | 78.44 |\n| NLBN + GL  | 22.50 | 29.31 |\n| NLBN + GD  | 15.17 | 19.83 |\n\n\n > Q2: The method is primarily a composition of existing components.\n\n A2: We agree that this work mainly focuses on system-level innovation; however, it also provides novel algorithms to address the occlusion issue and a height adjustment method to improve exploration efficiency. Also, we propose a new task, ECC, and a new platform, ECCD, that can inspire future research in this area.\n\n&zwnj;**Occlusion issues in crowds degrade the performance of detection and counting.**&zwnj; Occlusion is one of the key challenges in crowd counting, which requires an algorithm to actively adjust the viewpoint to mitigate this challenge. &zwnj;**To address this, the key component of the method, NLBN, is proposed to generate viewpoints according to the crowd distribution automatically.**&zwnj; It converts the difficult viewpoint selection problem into a relatively simple surface detection problem and generates viewpoints that reduce the degree of occlusion among the crowd. &zwnj;**Table 2 in the manuscript**&zwnj; shows that, when equipped with NLBN, ZECC significantly outperforms existing methods. &zwnj;**Table 1 in the Appendix**&zwnj; shows that NLBN reduces the obstruction rate of the line of sight, which contributes to the improved performance of ZECC.\n\n&zwnj;**A strategy utilizing height adjustment to improve exploration efficiency is proposed.**&zwnj; We explicitly consider the advantage of height adjustment in the exploration task. By using HAE, the agent obtains broad views, and irrelevant areas can be rapidly filtered out. This method mitigates redundant exploration caused by limited views at a fixed height and reduces the occlusion avoidance trajectory. &zwnj;**Figure 5 (a) in the manuscript**&zwnj; shows the efficiency improvement.\n\n&zwnj;**The system-level design serves as a foundation for future work.**&zwnj; ZECC not only serves as a baseline for further improvement but is also an automatic trajectory generation tool, which is important given the data scarcity in the embodied agent community [2]. Training-based methods can be developed based on ZECC to further improve performance.\n\n&zwnj;**A new task and dataset that supports this research.**&zwnj; ECCD is proposed, and it supports large-scale scenes and large object quantities simultaneously, enabling more complex algorithm designs.\n\n > Q3: Can the authors provide insights or preliminary evidence on real-world applicability?\n\n  A3: We show a real application case in response &zwnj;**A1**&zwnj;. The challenge of real-world deployment lies in dataset generation and long-range UAV control. These challenges are being solved by the community gradually. [2] shows that 3D Gaussian splatting can be used to construct simulation scenes based on the real world, and [3] has provided possible solutions for real-world drone deployment in large-scale environments. We can use these solutions to extend this work.\n\nWe still would like to emphasize that this work mainly focuses on solving the occlusion issue in crowd counting and improving exploration efficiency by using height information. We have demonstrated the effectiveness of the proposed methods in both simulation and a real-world application case. Sim-to-real techniques indeed are important to the community, and we are trying to improve these aspects in future works.\n\n > Q4: How critical is the vision-language model (GPT-4V) to the success of ZECC?\n\n A4: We conduct experiments on replacing GPT-4V with the open-source Qwen-VL-Max model and the tiny MiniGPT model. The results are shown in Table 2. It shows that the performance is consistent with different MLLMs. We believe this is because the images captured during HAE lack complex occlusion compared to LAE, making it easy to analyze, which results in consistent results for different MLLMs\n\nTable 2: Performance of different MLLMs.\n\n | Method        | MAPE (%)     | TD (m)           |\n|---------------|--------------|------------------|\n| Qwen-VL-Max   | 17.67 ± 2.4  | 3752.79 ± 94.9   |\n| MiniGPT       | 17.58 ± 3.12 | 3809.09 ± 123.69 |\n| GPT-4V        | 18.71 ± 1.41 | 3722.45 ± 73.78  |\n\n[1] Wang, Jianyuan et al. “VGGT: Visual Geometry Grounded Transformer.” Computer Vision and Pattern Recognition (2025).\n\n[2] Gao, Yunpeng et al. “OpenFly: A Comprehensive Platform for Aerial Vision-Language Navigation.” (2025).\n\n[3] Lou, Jiabin et al. “Air-M: A Visual Reality Many-Agent Reinforcement Learning Platform for Large-Scale Aerial Unmanned System.” 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023): 5598-5605."}, {"review_id": "4xg2NkO4je", "reviewer": "Reviewer_szRu", "summary": "The paper introduces a task called Embodied Crowd Counting (ECC), which tackles the long-standing issue of occlusion in crowd counting by shifting from passive to active perception. Instead of relying on static camera views, the authors propose deploying a mobile aerial agent (like a drone) that can dynamically navigate large-scale outdoor environments.\n\nTo support this task, they create ECCD, a realistic simulator featuring 60 virtual city-scale scenes with up to 15,000 individuals per scene. It uses a Poisson Point Process to simulate authentic crowd distributions. The authors also propose ZECC, a zero-shot agent that explores scenes using a two-stage approach: a Multi-modal Large Language Model (MLLM)-guided Active Top-down Exploration (ATE) for coarse crowd estimation and a Normal-line Based Navigation (NLBN) for refining observation points.\n\nExperiments claim to show ZECC achieves a strong balance between accuracy and efficiency compared to existing methods. Results show a commendable trade-off between performance and exploration cost, making a strong case for the real-world relevance of the proposed approach.", "questions": "1. Statistical Significance and Variance Reporting\n\nCould the authors include statistical metrics (e.g., standard deviation, confidence intervals) for the main experimental results, particularly across density levels or repeated trials with different random seeds or initial positions? Without this, it’s hard to evaluate the robustness of ZECC, especially since it leverages MLLMs, which can behave non-deterministically.\n\n2. Explainability and Failure Cases\n\nCould the authors provide visualizations or examples of failure modes (e.g., when ZECC undercounts/overcounts or mis-navigates due to occlusion or poor density estimation)? Including 2 to 3 such examples with interpretation (e.g., where ATE failed to prioritize a dense area, or NLBN chose suboptimal angles).\n\n3. The use of GPT-4V for determining exploration altitude is clever. But how consistent are its decisions? Is it queried once per scene or at every step?", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Quality:\n\nThe technical execution is thorough. ECCD is thoughtfully designed to simulate complex crowd distributions across diverse, large-scale scenes, addressing key limitations in prior datasets. The experimental section is well-detailed, with comparisons against standard baselines (ZSON, MVC), and ablations of ATE and NLBN components. However, the lack of statistical significance reporting (e.g., error bars, variance) is a notable gap, especially given the dynamic nature of the exploration process and the use of non-deterministic MLLMs.\n\n\nClarity:\n\nThe paper is generally well written, with clear illustrations. The architectural design of ZECC is easy to follow, and the motivation behind each module is well explained. That said, some parts, particularly the mathematical formulations for NLBN could benefit from more intuitive grounding or simplified descriptions. \n\n\nSignificance:\n\nThis work has high potential impact. By bridging the gap between embodied navigation and crowd analysis, it opens up a new research direction that aligns with real-world deployment scenarios. ECCD could serve as a benchmark to catalyze further innovation in this domain. That said, since the entire framework is still simulated, questions around transferability to real-world deployment (e.g., noisy sensors, unpredictable crowds) remain unanswered.\n\n\nOriginality:\n\nThe task definition, dataset design, and the approach are all fresh and well-motivated. While elements like Z-axis planning or crowd modeling with Poisson processes are known, their integration into a unified embodied framework for crowd counting is novel and impactful. ZECC’s zero-shot formulation is particularly a contribution, leveraging MLLMs without heavy retraining.", "was_revised": true, "final_justification": "I am maintaining my original score, which was already at the highest end in regards to the quality of the paper/work. The authors have provided clear and satisfactory responses during the rebuttal phase. While the questions raised were important, they served more as confirmation and follow-up to ensure the scientific rigor of the work, rather than indicating any serious concerns. The authors' replies demonstrated a strong understanding of the issues and confirmed that they are approaching the problem in a sound and principled manner. As such, I see no unresolved issues, and I continue to support acceptance of this submission.", "rebuttal": "> Q1: Could the authors include statistical metrics.\n \n A1: Thank you for the reviews to improve our work. We have updated the main experimental results with standard deviation in Table 1, Table 2, and Table 3. In each experiment, we generate three random starting positions within the navigable area. Additionally, the random seed is changed three times for each starting position, resulting in a total of nine trials for every experiment. The results show that the performance of ZECC is consistent.\n\n Table 1: Statistical performances.\n | Method                                                       | MAPE (%)     | TD (m)           |\n|--------------------------------------------------------------|--------------|------------------|\n| FBE + GL     | 57.19 ± 1.83 | 2513.06 ± 247.35 |\n| FBE + GD      | 53.38 ± 1.26 | 2513.06 ± 247.35 |\n| CoW + GL | 52.75 ± 1.52 | 3449.51 ± 127.2  |\n| CoW + GD | 46.01 ± 0.96 | 3449.51 ± 127.2  |\n| OpenFMNav + GL| 60.57 ± 2.43 | 5069.64 ± 183.23 |\n| OpenFMNav + GD | 49.41 ± 2.35 | 5069.64 ± 183.23 |\n| ZECC                                                         | 18.71 ± 1.41 | 3722.45 ± 73.78  |\n\n\nTable 2: MAPE under different crowd density\n\n| Methods   | L5            | L4            | L3            | L2            | L1            |\n|-----------|---------------|---------------|---------------|---------------|---------------|\n| FBE       | 65.13 ± 1.77  | 57.35 ± 1.28  | 51.58 ± 1.46  | 48.86 ± 1.36  | 37.58 ± 1.32  |\n| CoW       | 21.98 ± 1.39  | 35.45 ± 1.09  | 40.56 ± 0.57  | 34.85 ± 0.79  | 44.84 ± 1.55  |\n| OpenFMNav | 52.02 ± 1.22  | 45.73 ± 2.03  | 45.04 ± 2.62  | 33.93 ± 2.04  | 41.14 ± 2.83  |\n| ZECC      | 15.96 ± 0.75  | 16.13 ± 1.34  | 18.38 ± 1.09  | 26.14 ± 1.07  | 32.45 ± 0.71  |\n\nTable 3: TD under different crowd density levels\n\n| Methods   | L5            | L4            | L3            | L2            | L1            |\n|-----------|---------------|---------------|---------------|---------------|---------------|\n| FBE       | 2786.23 ± 233.01 | 2840.13 ± 229.27 | 2791.12 ± 240.75 | 2841.87 ± 289.77 | 2738.76 ± 276.85 |\n| CoW       | 3586.67 ± 155.39 | 4165.59 ± 162.44 | 3181.45 ± 143.66 | 3051.42 ± 164.31 | 2645.32 ± 137.58 |\n| OpenFMNav | 4838.67 ± 203.53 | 5714.58 ± 144.36 | 4787.69 ± 187.65 | 5894.42 ± 174.61 | 4193.41 ± 216.72 |\n| ZECC      | 4289.07 ± 68.94  | 3737.72 ± 75.73  | 3090.17 ± 53.6   | 2785.6 ± 109.92  | 2227.3 ± 107.59  |\n\n\n > Q2: Could the authors provide visualizations or examples of failure modes.\n\n A2: We provide a failure case in &zwnj;**Section 5 in the Appendix**&zwnj;. It shows that a rooftop occludes the MLLM and cannot realize the crowd beside the building. This is mainly due to the fact that current ATE lacks the ability to identify positions that can effectively observe positions relevant to targets, such as roads or squares, from a top-down view, resulting in navigation to irrelevant areas, such as rooftops. We will study this challenge in our future work.\n\n NLBN sometimes chooses suboptimal positions. In our experiments, this is mainly caused by the wrong detection results of the crowd counting model used to estimate the crowd distribution. It incorrectly identifies trees as a crowd in some cases, making NLBN generate navigation points near these target irrelevant areas. This can be solved by introducing a better target detection model to filter these cases or training a more robust crowd distribution estimator.\n\n > Q3: But how consistent are its decisions? Is it queried once per scene or at every step?\n\n A3: We provide statistical metrics of ZECC, and from the results in Table 2, Table 3 and Table 4, it shows that the result is consistent. We believe this is because crowds can be easily identified during HAE in most cases, as the drone's top-down view provides a clear sight, enabling the MLLM to make consistent decisions. \n\n The MLLM used in ZECC is used to decide whether to conduct LAE in each HAE step."}, {"review_id": "vTMZwf7NSw", "reviewer": "Reviewer_dZWy", "summary": "The paper introduces Embodied Crowd Counting (ECC), a new task that frames crowd counting as an active vision problem for a drone agent. The papers build ECCD, a 60-scene Unreal-Engine simulator that combines large outdoor areas with up to 15k people. They propose ZECC, a zero-shot baseline, including Active Top-down Exploration, Normal-Line-Based Navigation, and Fine Detections. Experiments show that the proposed method delivers a favourable trade-off between counting accuracy and navigation cost when compared with classical frontier exploration, zero-shot object-navigation agents, and a multi-view counting baseline.", "questions": "Please refer to weaknesses.", "limitations": "No, there are no real-world experiments to implement this model.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The paper defines a novel task—Embodied Crowd Counting—that recasts crowd analysis as an active-vision problem for mobile agents. \n2. By bridging embodied AI and crowd counting, the work opens a new research direction and provides resources that can stimulate further studies in both communities.\n\nWeaknesses\n1. All evaluations are confined to the synthetic ECCD simulator. The real-world transferability of both the dataset and the ZECC policy remains unverified. \n2. Please show the reasons for building embodied crowd counting instead of embodied small object detection. I think the latter is more valuable with location information.\n3. The crowd generator relies on a simple Poisson point process, which ignores structured formations common in real gatherings (queues, flows, social spacing).\n4. Comparative experiments omit recent multi-view crowd-counting methods (“Cross-View Cross-Scene Multi-View Crowd Counting,” and “CountFormer: Multi-View Crowd Counting Transformer”), making it hard to judge the improvement of ZECC.\n5. ZSON appears without definition.", "was_revised": true, "final_justification": "Although I think it is far away to real embodied application, the research is exploratory study. I revise my rating to positive.", "rebuttal": "> Q1: All evaluations are confined to the synthetic ECCD simulator.\n\n A1: Thank you for your review to improve our work. We have provided a real-world application case in &zwnj;**Appendix 6**&zwnj; to test ZECC's capability in reducing occlusions in densely crowded scenarios. Besides, we provide results in another real-world scenario, which is denser compared to the existing one. The drone model used is the Taobotics Q300. The captured images are transmitted back to a ground server using a remote communication protocol and fed into VGGT [1] to estimate relative point clouds and poses. The proposed NLBN is then employed to calculate relative navigation points. The drone's absolute geographic coordinates and poses are recorded in real-time by its GPS. By leveraging the relationships between absolute and relative geographic coordinates and poses, the navigation points calculated by NLBN are mapped to absolute navigation points. After the ground server controls the drone to fly to these absolute navigation points, it captures images of the crowd. We utilize Grounding DINO and the crowd counting model Generalized Loss to perform crowd detection or counting on images captured from both distant and close-up perspectives, respectively. The ground truth for crowd annotations is manually labeled.\n\n > Q2: Please show the reasons for building embodied crowd counting instead of embodied small object detection. I think the latter is more valuable with location information.\n\n A2: It is indeed that ZECC leverages the concept of small object detection. However, we think that crowds often appear with heavy density and occlusion, which makes conducting embodied tasks on crowds more challenging. These complex distributions require additional techniques like NLBN to generate fine viewpoints to gain accurate counting results. We will broaden our scope to general small object detection in our future work.\n\n > Q3: The crowd generator relies on a simple Poisson point process, which ignores structured formations common in real gatherings (queues, flows, social spacing).\n\n A3: The current ECCD includes scenes that simulate real crowd distribution, such as queues and flows. As mentioned in &zwnj;**line 159**&zwnj; in the manuscript, the Poisson point process only determines the number and distribution of individuals &zwnj;**in a block**&zwnj;, and the shape and distribution of the blocks are adjusted by human experts according to the environment semantics. In this way, crowds in street scenes appear to be in queues, and crowds in intersections appear to be in flows.\n\n > Q4: Comparative experiments omit recent multi-view crowd-counting methods (\"Cross-View Cross-Scene Multi-View Crowd Counting,\" and \"CountFormer: Multi-View Crowd Counting Transformer\"), making it hard to judge the improvement of ZECC.\n\n A4: Since no open source code for [2] is found, we compare with [3] using the same settings as MVC. The weight of the models are fixed during testing. The results are shown in Table 1. Although recent MVC methods show improvement, they still suffer from significant degradation when the number of cameras decreases. MVC methods cannot fundamentally solve the occlusion issue because of fixed cameras. This result illustrates the necessity of embodied crowd counting.\n\nTable 1: Comparison with different MVC methods\n| Method          | MAPE (%)    | \\# of Cameras |\n|-----------------|-------------|----------------|\n| MVC-10          | 15.13 ± 0.00| 1735.32 ± 0.00 |\n| MVC-20          | 39.92 ± 0.00| 747.32 ± 0.00  |\n| MVC-30          | 61.43 ± 0.00| 333.32 ± 0.00  |\n| CountFormer-10  | 12.8 ± 0.00 | 1735.32 ± 0.00 |\n| CountFormer-20  | 35.26 ± 0.00| 747.32 ± 0.00  |\n| CountFormer-30  | 56.76 ± 0.00| 333.32 ± 0.00  |\n| ZECC            | 18.71 ± 1.41| 5 ± 0.00       |\n\n> Q5: ZSON appears without definition.\n\nA5: We apologize for this typo mistake. ZSON is defined in [4]. We will add this definition to the revised paper.\n\n[1] Wang, Jianyuan et al. \"VGGT: Visual Geometry Grounded Transformer.\" Computer Vision and Pattern Recognition (2025).\n\n[2] Zhang, Qi et al. \"Cross-View Cross-Scene Multi-View Crowd Counting.\" 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021): 557-567.\n\n[3] Mo, Hong et al. \"CountFormer: Multi-View Crowd Counting Transformer.\" ArXiv abs/2407.02047 (2024): n. pag.\n\n[4] Majumdar, Arjun et al. “ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings.” ArXiv abs/2206.12403 (2022): n. pag."}, {"review_id": "QL2EMojDdz", "reviewer": "Reviewer_nYLZ", "summary": "This paper proposes a new task called Embodied Crowd Counting (ECC). This task introduces an active camera setting into crowd counting, aiming to address the fundamental occlusion issue in traditional image-based crowd counting. To enable the task, this paper proposes Embodied Crowd Counting Dataset (ECCD), a simulation environment combines 60 large scale scenes and large number of crowds simultaneously. The crowd distribution is modeled by Poisson Point Process, a method to mimic realistic crowd distribution. Based on ECCD, an agentic method, Zero-shot Embodied Crowd Counting (ZECC), is proposed. This method first uses Active Top-down Exploration (ATE), a module based on Multi-modal Large Language Model (MLLM), to efficiently estimate coarse crowd distribution by adjusting exploration altitude. Then, Normal-line Based Navigation (NLBN) is used to conduct fine analysis of the crowd structure to avoid potential occlusion and enhance visibility, resulting in improved counting performance. Extensive experiments show that ZECC achieves strong performance against existing navigation-based methods and image-based counting methods while maintaining efficiency.", "questions": "See weaknesses.", "limitations": "The limitations are described in the paper.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1)\tThe proposed ECC is novel. The introduction of embodied agent setting to crowd counting is well-motivated.\n2)\tECCD is significantly different from existing dataset in both crowd counting and navigation with interactive ability and large target quantity and complex distribution.\n3)\tZECC utilizes vertical movement to balance exploration cost and performance while analyzing crowd considering its 3D structure, which are not considered by existing methods and provides new perspectives in both navigation and crowd counting.\n4)\tThe experiment results are comprehensive. The authors have demonstrated effectiveness of ZECC by comparing with existing ZSON and crowd counting methods, and the effectiveness of ATE and NLBN and the performance-cost trade-off are also illustrated. More experimental results in the Appendix further give analysis on the mechanism of ZECC reducing occlusion. These results comprehensively demonstrate the effectiveness of ZECC.\n5)\tOverall, this paper provides a novel perspective to both embodied navigation and crowd counting, and demonstrates its effectiveness convincedly, which can promote further progress in the fields.\n\n\nWeaknesses:\n1)\tThe paper does not explain ‘VPS’ and ‘ATE-VPS’ in Table 5, which makes this experiment confusing. This experiment seems to demonstrate the effectiveness of ATE viewpoint; however, a detailed explanation is necessary. \n2)\tZECC contains numerous calculation steps and components such as ATE, NLBN, VPS, etc. It is suggested to include an algorithm chart for clarity.\n3)\tIn the failure case study, the author mentioned that ATE is vulnerable in some occlusion cases. This may restrict performance of ZECC. A further discussion on this limitation would strength the paper.\n4)\tMinor weaknesses: The authors should define the task names in Table 1. ‘ZSON’ in line 132 is not clearly defined when first time used. Also, the reference is missing. \n5)\tIn line 225, a space is missing between ‘Figure 2’ and ‘(b)’.", "was_revised": true, "final_justification": "Thanks for the rebuttal. My major concerns have been addressed well. I tend to accept this paper.", "rebuttal": "> Q1: The paper does not explain ‘VPS’ and ‘ATE-VPS’ in Table 5, which makes this experiment confusing. This experiment seems to demonstrate the effectiveness of ATE viewpoint; however, a detailed explanation is necessary.\n\n A1: We apologize for this typo. 'w/o VPS' refers to not using view point selection (VPS) but select a point along the normal\n vector with **ε**. w/o ATE-VPS refers to not using the ATE-\nview-vector-based view point selection (ATE-VPS), but\nrandomly select a navigation vector from the potential\nnavigation vectors. We will add these explanation to the revised paper.\n\n > Q2: ZECC contains numerous calculation steps and components such as ATE, NLBN, VPS, etc. It is suggested to include an algorithm chart for clarity.\n\n A2: We provide the algorithm in Algorithm 1 and Algorithm 2. We will add these explanation to the revised paper.\n\nAlgorithm 1 Pseudo-Code of ATE\n\n**Require:** Global distribution $D$, HAE map $M_H$, LAE map $M_L$, Prompt $I$\n\n**Ensure:**\n\n1: $t \\leftarrow 0$;\n\n2: $done1 \\leftarrow False$;\n\n3: $D \\leftarrow None$\n\n4: $M_H \\leftarrow None$\n\n5: $M_L \\leftarrow None$\n\n6: **while** not $done1$ **do**\n\n7:   $O_t, p_t \\leftarrow getState()$;\n\n8:   $s_t \\leftarrow MLLM(O_t; I)$;\n\n9:   $M_H \\leftarrow updateMap(O_t; p_t)$;\n\n10:   **if** $s_t > 0.5$ **then**\n\n11:     $toLAE()$;\n\n12:     $done2 \\leftarrow False$;\n\n13:     **while** not $done2$ **do**\n\n14:       $O_f, p_f \\leftarrow getState()$;\n\n15:       $d_f \\leftarrow P(G(O_f), p_f)$;\n\n16:       $M_L \\leftarrow updateMap(O_f; p_f)$;\n\n17:       $D \\leftarrow updateDistribution(D, d_f)$;\n\n18:       $done2 \\leftarrow toUnexplored(M_L)$;\n\n19:     **end while**\n\n20:   **end if**\n\n21:   $done1 \\leftarrow toUnexplored(M_H)$;\n\n22:   $t \\leftarrow t + 1$;\n\n23: **end while**\n\n24: **return** $D$\n\n\nAlgorithm 2 Pseudo-Code of NLBN\n\n**Require:** Cluster size $\\epsilon$, Navigation vector degree $\\zeta$, Navigation point range $\\eta$, Density map threshold $\\kappa$, Global distribution $D$\n\n**Ensure:**\n\n1: $D \\leftarrow \\text{ATE}(D)$;\n\n2: $\\{ \\mathbf{x}_i^{\\text{cluster}}, \\ldots, \\mathbf{x}_N^{\\text{cluster}} \\} \\leftarrow \\text{Cluster}(D)$;\n\n3: $done \\leftarrow False$;\n\n4: **for** $i = 1$ to $N$ **do**\n\n5:   $d_i^{\\text{cluster}} \\leftarrow \\text{FitPlane}(\\mathbf{x}_i^{\\text{cluster}})$;\n\n6:   $\\mathbf{x}_i^{\\text{ATE}} \\leftarrow \\text{getViewpoint}(D)$;\n\n7:   Compute $\\{ d_{i1}^{\\text{view}}, \\ldots, d_{im}^{\\text{view}} \\}_i$ using Eq. (4);\n\n8:   Compute $d_i^{\\text{ATE}}$ using Eq. (5);\n\n9:   Compute $d_i^{\\text{view}}$ using Eq. (6);\n\n10:   Compute $\\mathbf{x}_i^{\\text{view}}$ using Eq. (7);\n\n11: **end for**\n\n12: **while** not $done$ **do**\n\n13:   $done \\leftarrow \\text{toNextNaviPoint}(\\{ \\mathbf{x}_i^{\\text{view}}, \\ldots, \\mathbf{x}_N^{\\text{view}} \\})$\n\n14: **end while**\n\n > Q3: In the failure case study, the author mentioned that ATE is vulnerable in some occlusion cases. This may restrict performance of ZECC. A further discussion on this limitation would strength the paper.\n\n A3: We believe that this is mainly due to the fact that current ATE lacks the ability to identify positions that can effectively observe positions relevant to targets, such as roads or squares, from a top-down view, resulting in navigation to irrelevant areas, such as rooftops. We will study this challenge in our future work.\n\n > Q4: Minor weaknesses: The authors should define the task names in Table 1. ‘ZSON’ in line 132 is not clearly defined when first time used. Also, the reference is missing.\n\n A4: We apologize for this typo. ZSON is defined in [1]. We will add this definition to the revised paper.\n\n > Q5: In line 225, a space is missing between ‘Figure 2’ and ‘(b)’.\n\n A5: We apologize for this typo. We will revise this part in the paper.\n\n [1] Majumdar, Arjun et al. “ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings.” ArXiv abs/2206.12403 (2022): n. pag."}]

Author rebuttal:

Meta-review: Initially the reviews were mixed but, after the rebuttal and discussion phase, they all recommend acceptance.
Decision: Accept (poster)