A Review of the Recent Progress on Crowd Anomaly Detection

Abstract: Surveillance videos are crucial in imparting public security, reducing or avoiding the accidents that occur from anomalies. Crowd anomaly detection is a rapidly growing research field that aims to identify abnormal or suspicious behavior in crowds. This paper provides a comprehensive review of the state-of-the-art in crowd anomaly detection and, different taxonomies, publicly available datasets, challenges, and future research directions. The paper first provides an overview of the field and the importance of crowd anomaly detection in various applications such as public safety, transportation, and surveillance. Secondly, it presents the components of crowd anomaly detection and its different taxonomies based on the availability of labels, and the type of anomalies. Thirdly, it presents the review of the recent progress of crowd anomaly detection. The review also covers publicly available datasets commonly used for evaluating crowd anomaly detection methods. The challenges faced by the field, such as handling variability in crowd behavior, dealing with large and complex data sets, and addressing the imbalance of data, are discussed. Finally, the paper concludes with a discussion of future research directions in crowd anomaly detection, including integrating multiple modalities, addressing privacy concerns, and addressing crowd monitoring systems’ ethical and legal implications.

Sarah Altowairqi, Suhuai Luo and Peter Greer. “A Review of the Recent Progress on Crowd Anomaly Detection”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.4 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140472

@article{Altowairqi2023,
title = {A Review of the Recent Progress on Crowd Anomaly Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140472},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140472},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Sarah Altowairqi and Suhuai Luo and Peter Greer}
}