Detection of Criminal Behavior at the Residential Unit based on Deep Convolutional Neural Network

Studies on abnormal behavior based on deep learning as a processing platform increase. Deep learning, specifically the convolutional neural network (CNN), is known for learning the features directly from the raw image. In return, CNN requires a high-performance hardware platform to accommodate its computational cost like AlexNet and VGG-16 with 62 million and 138 million parameters, respectively. Hence in this study, four CNN samplings with different architectures in detecting abnormal behavior at the gate of residential units are evaluated and validated. The forensic postures, with some other collected data, are used for the preliminary step in constructing the criminal case database. High accuracy up to 97% is obtained from the trained CNN samplings with 80% to 97% recognition rate achieved during the offline testing and 70% to 90% recognition rate recorded during the real-time testing. Results showed that the developed CNN samplings owned good performance and can be utilized in detecting and recognizing the normal and abnormal behavior at the gate of residential units.

@article{Razak2022,
  title     = {Detection of Criminal Behavior at the Residential Unit based on Deep Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {H. A. Razak and Nooritawati Md Tahir and Ali Abd Almisreb and N. K. Zakaria and N. F. M. Zamri},
  doi       = {10.14569/IJACSA.2022.0130293},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130293}
}