Crowded Scene Analysis: A Survey
arXiv:1502.01812 · doi:10.1109/TCSVT.2014.2358029
Abstract
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.
20 pages in IEEE Transactions on Circuits and Systems for Video Technology, 2015
References in corpus (2)
Cited by in corpus (29)
- A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
- HA-CCN: Hierarchical Attention-based Crowd Counting Network
- Crowd Behavior Analysis: A Review where Physics meets Biology
- CNN-based Density Estimation and Crowd Counting: A Survey
- A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds
- Crowd counting with crowd attention convolutional neural network
- Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review
- Fast Video Crowd Counting with a Temporal Aware Network
- Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues
- Perspective-Guided Convolution Networks for Crowd Counting
- CrowdEstimator: Approximating Crowd Sizes with Multi-modal Data for Internet-of-Things Services
- The Rise of Technology in Crime Prevention: Opportunities, Challenges and Practitioners Perspectives
- Crowd counting with segmentation attention convolutional neural network
- Online Pedestrian Group Walking Event Detection Using Spectral Analysis of Motion Similarity Graph
- Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance
- sZoom: A Framework for Automatic Zoom into High Resolution Surveillance Videos
- An Adaptive Training-less System for Anomaly Detection in Crowd Scenes
- M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance
- Segmentation of laterally symmetric overlapping objects: application to images of collective animal behaviour
- Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
- Efficient Crowd Counting via Structured Knowledge Transfer
- Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting
- Semantic segmentation of trajectories with improved agent models for pedestrian behavior analysis
- MPASNET: Motion Prior-Aware Siamese Network for Unsupervised Deep Crowd Segmentation in Video Scenes
- The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation
- Automated Lane Detection in Crowds using Proximity Graphs
- Improved Activity Forecasting for Generating Trajectories
- Spatio-temporal interaction model for crowd video analysis
- A Promotion Method for Generation Error Based Video Anomaly Detection