Crowd analysis: a survey

By Beibei Zhan, Dorothy Ndedi Monekosso, Paolo Remagnino, Sergio A. Velastin, Li-Qun Xu. Machine Vision and Applications, 2008

604 citations recorded by the source corpus.

In the year 1999 the world population reached 6 billion, doubling the previous census estimate of 1960. Recently, the United States Census Bureau issued a revised forecast for world population showing a projected growth to 9.4 billion by 2050 (US Census Bureau, http://www.census.gov/ipc/www/worldpop.html). Different research disci- plines have studied the crowd phenomenon and its dynamics from a social, psychological and computational standpoint respectively. This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/b03420aa079b8d4c5332f369b21dc997723e23db

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Citations in the corpus

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].”
Multiple object tracking: A literature review (2014, methodology): “Inspired by the crowd simulation literature [23], motion patterns are introduced to alleviate the difficulty of tracking an individual object in the crowd.”
Spatiotemporal Group Context for Pedestrian Counting (2014, background): “Traditional approaches treat pedestrian counting as a classic object detection problem.”
A survey of video datasets for human action and activity recognition (2013, background): “In the limit, there are some studies devoted to the analysis of the interaction of multiple people (crowd behaviour) [237].”
Crowded Scene Analysis: A Survey (2015, methodology): “Zhan et al. [15] presented a survey in 2008 on crowd analysis methods in computer vision.”
Crowd Analysis Using Computer Vision Techniques (2010, background): “It should be noted that survey papers on crowd analysis have been proposed in the past, such as [4].”
Abnormal crowd behavior detection using social force model (2009, background): “In [6] a review of the latest research trends and approaches from different research communities is provided.”
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.”
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies (2017, methodology): “Crowd motion pattern models are another type of interaction models used in MOT, inspired by the crowd simulation literature [79, 61].”
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020, methodology): “Furthermore, algorithms developed for the purpose of crowd analytics have found applications in other fields such as agriculture monitoring [18], microscopic biology [19], urban planning and environmental survey [2, 20].”
Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs (2017, methodology): “Also, the methods developed for crowd counting can be extended to counting tasks in other fields such as cell microscopy [38, 36, 16, 6], vehicle counting [23, 49, 48, 11, 34], environmental survey [8, 43], etc.”
A survey on activity recognition and behavior understanding in video surveillance (2013, neutral): “Zhan 08 Crowd analysis [224]”
Going deeper into action recognition: A survey (2016, background): “…(2006) human motion capture and analysis Yilmaz et al. (2006) object detection and tracking Turaga et al. (2008) human actions, complex activities Zhan et al. (2008) surveillance and crowd analysis Poppe (2010) human action recognition Weinland et al. (2011) action recognition Aggarwal (2011)…”
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.”
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].”
The Geography of Taste: Analyzing Cell-Phone Mobility and Social Events (2010, methodology): “Following [5, 6] we propose to organize crowd modeling according to three levels: microscopic, macroscopic, mesoscopic.”