# Crowded Scene Analysis: A Survey

arXiv: https://arxiv.org/abs/1502.01812 · HTML: https://mlsanity.com/paper/1502.01812/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2015.

## Built on by (most cited first, 21 of 21)

- [Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things](https://mlsanity.com/paper/2301.03561/) (2023)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation](https://mlsanity.com/paper/2108.02980/) (2021)
- [Crowd counting with crowd attention convolutional neural network](https://mlsanity.com/paper/2204.07347/) (2022)
- [Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks](https://mlsanity.com/paper/2212.11542/) (2022)
- [IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2311.15193/) (2023)
- [Visual Crowd Analysis: Open Research Problems](https://mlsanity.com/paper/2308.10677/) (2023)
- [PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement](https://mlsanity.com/paper/2309.11125/) (2023)
- [Shifted Autoencoders for Point Annotation Restoration in Object Counting](https://mlsanity.com/paper/2312.07190/) (2023)
- [Rethinking Trajectory Prediction via "Team Game"](https://mlsanity.com/paper/2210.08793/) (2022)
- [Deep Neural Networks in Video Human Action Recognition: A Review](https://mlsanity.com/paper/2305.15692/) (2023)
- [Decoupling What to Count and Where to See for Referring Expression Counting](https://mlsanity.com/paper/2510.24374/) (2025)
- [MPASNET: Motion Prior-Aware Siamese Network for Unsupervised Deep Crowd Segmentation in Video Scenes](https://mlsanity.com/paper/2101.08609/) (2021)
- [Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting](https://mlsanity.com/paper/2108.02970/) (2021)
- [Crowd counting with segmentation attention convolutional neural network](https://mlsanity.com/paper/2204.07380/) (2022)
- [Vision-based Conflict Detection within Crowds based on High-Resolution Human Pose Estimation for Smart and Safe Airport](https://mlsanity.com/paper/2207.00477/) (2022)
- [Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives](https://mlsanity.com/paper/2307.13541/) (2023)
- [Normality Prior Guided Multi-Semantic Fusion Network for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2506.18544/) (2025)
- [Trajectory Prediction in Dynamic Object Tracking: A Critical Study](https://mlsanity.com/paper/2506.19341/) (2025)
- [Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning](https://mlsanity.com/paper/2606.09289/) (2026)
- [Incremental Optimal Assignment for Real-Time Crowd Tracking](https://mlsanity.com/paper/2607.21368/) (2026)

## Abstract (arXiv)

Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. In the past few years, an increasing number of works on crowded scene analysis have been reported, covering different aspects including crowd motion pattern learning, crowd behavior and activity analysis, and anomaly detection in crowds. This paper surveys the state-of-the-art techniques on this topic. We first provide the background knowledge and the available features related to crowded scenes. Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.