Title: Embodied Crowd Counting
Venue: NeurIPS 2025
Accepted: yes
Keywords: crowd counting

embodied AI
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?.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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)’.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nThe limitations are described in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nPlease refer to weaknesses.\n\nLIMITATIONS:\nNo, there are no real-world experiments to implement this model.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nQuality:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

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