Unusual Human Behavior Detection System in Real-Time Video Systems

Abnormal behavior detection, in terms of importance, has become a necessity in real-time visual systems. The main problem is the ambiguity in the difference between the characteristics of abnormal and normal behavior, which its definition is usually different according to the previous context of images. In this research, three approaches are used. In the first approach, a standard Convolutional Automatic Encoder (CAE) is used. After evaluation, it was found that the standard CAE problem is that it does not take into account the temporal aspect of the image frames sequence. The second method involves automatic encoding to learn the dataset's spatio-temporal structures. In the third approach, the complex LSTM cells are used for further improvement. The outcomes of the test display that the proposed methods have better performance compared to many of the previous conventional methods, and their efficiency in identifying abnormal behavior is very competitive compared to previous methods.

@article{Bu2023,
  title     = {Unusual Human Behavior Detection System in Real-Time Video Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Yanbin Bu and Ting Chen and Hongxiu Duan and Mei Liu and Yandan Xue},
  doi       = {10.14569/IJACSA.2023.01406104},
  url       = {https://doi.org/10.14569/IJACSA.2023.01406104}
}