Liu, Hai Chuan and Chuah, Joon Huang and Mohd Khairuddin, Anis Salwa and Zhao, Xian Min and Wang, Xiao Dan (2023) Campus abnormal behavior recognition with temporal segment transformers. IEEE Access, 11. pp. 38471-38484. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2023.3266440.
Full text not available from this repository.Abstract
The intelligent campus surveillance system is beneficial to improve safety in school. Abnormal behavior recognition, a field of action recognition in computer vision, plays an essential role in intelligent surveillance systems. Computer vision has been actively applied to action recognition systems based on Convolutional Neural Networks (CNNs). However, capturing sufficient motion sequence features from videos remains a significant challenge in action recognition. This work explores the challenges of video-based abnormal behavior recognition on campus. In addition, a novel framework is established on long-range temporal video structure modeling and a global sparse uniform sampling strategy that divides a video into three segments of identical durations and uniformly samples each snippet. The proposed method incorporates a consensus of three temporal segment transformers (TST) that globally connects patches and computes self-attention with joint spatiotemporal factorization. The proposed model is developed on the newly created campus abnormal behavior recognition (CABR50) dataset, which contains 50 human abnormal action classes with an average of over 700 clips per class. Experiments show that it is feasible to implement abnormal behavior recognition on campus and that the proposed method is competitive with other peer video recognition in terms of Top-1 and Top-5 recognition accuracy. The results suggest that TST-L+ can improve campus abnormal behavior recognition, corresponding to Top-1 and Top-5 accuracy results of 83.57% and 97.16%, respectively.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Behavioral sciences; Videos; Transformers; Three-dimensional displays; Solid modeling; Computational modeling; Spatiotemporal phenomena; Action recognition; campus abnormal behavior; computer vision; motion sequence features; temporal segment transformer |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 04 Jul 2023 06:32 |
| Last Modified: | 04 Jul 2023 06:32 |
| URI: | http://eprints.um.edu.my/id/eprint/39040 |
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