Crowded Scene Analysis: A Survey

By Teng Li, Huan Chang, Meng Wang, Bingbing Ni, Richang Hong, Shuicheng Yan. IEEE transactions on circuits and systems for video technology (Print), 2015

485 citations recorded by the source corpus.

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 the crowded scene analysis have been reported, which covered different aspects including crowd motion pattern learning, crowd behavior and activity analyses, 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, and system performance are provided corresponding to different aspects of the crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1018e1e0ceda44e0f643b376d008934cdfae8c0f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs (2017, background): “With ubiquitous usage of surveillance cameras and advances in computer vision, crowd scene analysis [18, 43] has gained a lot of interest in the recent years.”
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit (2018, background): “With the rapid increase of population of major cities, crowd scene analysis [11, 33] has already become an important security technique in video surveillance [16, 20, 36].”
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020, background): “Consequently, computer vision-based crowd analytics and surveillance [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] have received increased interest.”
CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting (2017, background): “A more comprehensive survey of different crowd counting methods can be found in [6, 13].”
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (2017, methodology): “Researchers have attempted to provide a comprehensive survey and evaluation of existing techniques for various aspects of crowd analysis [105, 30, 44, 55, 117].”
Scale Aggregation Network for Accurate and Efficient Crowd Counting (2018, background): “With the rapid growth of the urban population, crowd scene analysis [1,2] has gained considerable attention in recent years.”
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes (2018, background): “Growing number of network models have been developed [1, 2, 3, 4, 5] to deliver promising solutions for crowd flows monitoring, assembly controlling, and other security services.”
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos (2018, background): “A detailed review of abnormal human behavior and crowd motion analysis is provided in [17] and [18].”