Advances in abnormal human activity detection based on video surveillance

Shilong Wang; Maylor Karhang Leung; Siak Wang Khor; Hasnain Sultan

doi:10.1117/12.3011981

7 December 2023 Advances in abnormal human activity detection based on video surveillance

Shilong Wang, Maylor Karhang Leung, Siak Wang Khor, Hasnain Sultan

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294140 (2023) https://doi.org/10.1117/12.3011981
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

This research reviewed the rapidly developing field of abnormal behavior detection in video surveillance. The emergence of deep learning techniques, especially in video feature extraction, makes up for the shortcomings of traditional methods. In terms of abnormal behavior detection, unsupervised, supervised, and weakly supervised methods have different advantages and disadvantages. At present, the weakly supervised method is popular in this field, and the highest AUC under the UCF dataset reaches 86.98%. Benchmark datasets play a crucial role in evaluating the performance of algorithms. Future research will focus on addressing challenges related to complex scene dynamics, occlusions, and real-time processing. Integrating multiple sensing modalities, transfer learning, deep learning techniques for feature extraction, and leveraging spatiotemporal graph-based methods are important directions for improving surveillance systems in anomaly detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shilong Wang, Maylor Karhang Leung, Siak Wang Khor, and Hasnain Sultan "Advances in abnormal human activity detection based on video surveillance", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294140 (7 December 2023); https://doi.org/10.1117/12.3011981

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