Crowd Analysis in Video Surveillance: A Review

Tomar, Ankit; Kumar, Santosh; Pant, Bhasker

doi:10.1109/dasa54658.2022.9765008

Cited by 16 publications

(4 citation statements)

References 46 publications

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“…In recent years, deep learning has emerged as a popular approach to addressing the challenges of HAR. Deep learning techniques provide increased flexibility and effectiveness in analyzing and understanding the patterns of human motion [102]. HAR applications briefly shown in Table 4.…”

Section: Harmentioning

confidence: 99%

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir,

Kim

2023

Electronics

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Smart cities are being developed worldwide with the use of technology to improve the quality of life of citizens and enhance their safety. Video surveillance is a key component of smart city infrastructure, as it involves the installation of cameras at strategic locations throughout the city for monitoring public spaces and providing real-time surveillance footage to law enforcement and other city representatives. Video surveillance systems have evolved rapidly in recent years, and are now integrated with advanced technologies like deep learning, blockchain, edge computing, and cloud computing. This study provides a comprehensive overview of video surveillance systems in smart cities, as well as the functions and challenges of those systems. The aim of this paper is to highlight the importance of video surveillance systems in smart cities and to provide insights into how they could be used to enhance safety, security, and the overall quality of life for citizens.

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Section: Harmentioning

confidence: 99%

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir,

Kim

2023

Electronics

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“…This helps alert staff to observe relevant areas, support the detection of hazardous events, and gather evidence [ 4 ]. Currently, CMA technology primarily focuses on the processing of video data [ 5 , 6 , 7 ]. The related methods can be categorized into two main types: pattern recognition as well as machine learning [ 8 , 9 ], and physical heuristic methods [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Crowd Movement Analysis Method Based on Radar Particle Flow

Zhang,

Cao,

Zhao

et al. 2024

Sensors

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Crowd movement analysis (CMA) is a key technology in the field of public safety. This technology provides reference for identifying potential hazards in public places by analyzing crowd aggregation and dispersion behavior. Traditional video processing techniques are susceptible to factors such as environmental lighting and depth of field when analyzing crowd movements, so cannot accurately locate the source of events. Radar, on the other hand, offers all-weather distance and angle measurements, effectively compensating for the shortcomings of video surveillance. This paper proposes a crowd motion analysis method based on radar particle flow (RPF). Firstly, radar particle flow is extracted from adjacent frames of millimeter-wave radar point sets by utilizing the optical flow method. Then, a new concept of micro-source is defined to describe whether any two RPF vectors originated from or reach the same location. Finally, in each local area, the internal micro-sources are counted to form a local diffusion potential, which characterizes the movement state of the crowd. The proposed algorithm is validated in real scenarios. By analyzing and processing radar data on aggregation, dispersion, and normal movements, the algorithm is able to effectively identify these movements with an accuracy rate of no less than 88%.

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“…On the other hand, recent research has explored the potential of implementing emotion recognition technology based on deep learning (DL) [27][28][29] in video surveillance systems for large groups of people. These studies have shown promising results in detecting emotions such as Anger, Fear, Joy, and Sadness from facial expressions.…”

Section: Introductionmentioning

confidence: 99%

Facial Emotion Recognition for Photo and Video Surveillance Based on Machine Learning and Visual Analytics

Kalyta,

Barmak,

Radiuk

et al. 2023

Applied Sciences

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Modern video surveillance systems mainly rely on human operators to monitor and interpret the behavior of individuals in real time, which may lead to severe delays in responding to an emergency. Therefore, there is a need for continued research into the designing of interpretable and more transparent emotion recognition models that can effectively detect emotions in safety video surveillance systems. This study proposes a novel technique incorporating a straightforward model for detecting sudden changes in a person’s emotional state using low-resolution photos and video frames from surveillance cameras. The proposed technique includes a method of the geometric interpretation of facial areas to extract features of facial expression, the method of hyperplane classification for identifying emotional states in the feature vector space, and the principles of visual analytics and “human in the loop” to obtain transparent and interpretable classifiers. The experimental testing using the developed software prototype validates the scientific claims of the proposed technique. Its implementation improves the reliability of abnormal behavior detection via facial expressions by 0.91–2.20%, depending on different emotions and environmental conditions. Moreover, it decreases the error probability in identifying sudden emotional shifts by 0.23–2.21% compared to existing counterparts. Future research will aim to improve the approach quantitatively and address the limitations discussed in this paper.

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Crowd Analysis in Video Surveillance: A Review

Cited by 16 publications

References 46 publications

A Survey of Video Surveillance Systems in Smart City

A Survey of Video Surveillance Systems in Smart City

A Crowd Movement Analysis Method Based on Radar Particle Flow

Facial Emotion Recognition for Photo and Video Surveillance Based on Machine Learning and Visual Analytics

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