@article{3039,
  keywords = {Volume Crime Classification, Crime Detection, Malicious Activity Detection, Deep Learning},
  author = {Atif Jan and Gul Muhammad Khan},
  title = {Real World Anomalous Scene Detection and Classification using Multilayer Deep Neural Networks},
  abstract = {Surveillance videos record malicious events in a locality utilizing various machine learning algorithms for detection. Deep-learning algorithms being the most prominent AI algorithms are data-hungry as well as computationally expensive. These algorithms perform better when trained over a diverse and huge set of examples. These modern AI methods have a dire need of utilizing human intelligence to pamper the problem in such a way as to reduce the ultimate effort in terms of computational cost. In this research work, a novel methodology termed Bag of Focus (BoF) based training methodology has been proposed. BoF is based on the concept of selecting motion-intensive blocks in a long video, for training different deep neural networks (DNN's). The methodology reduced the computational overhead by 90% (ten times) in comparison to when full-length videos are entertained. It has been observed that training networks using BoF are equally effective in terms of performance for the same network trained over the full-length dataset. In this research work, firstly, a fine-grained annotated dataset including instance and activity information has been developed for real-world volume crimes. Secondly, a BoF-based methodology has been introduced for effective training of the state-of-the-art 3D, and 2D Convolutional Neural Networks (CNNs). Lastly, a comparison between the state-of-the-art networks have been presented for malicious event recognition in videos. It has been observed that 2D CNN even with lesser parameters achieved a promising classification accuracy of 98.7% and Area under the curve (AUC) of 99.7%.},
  year = {2023},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {8},
  chapter = {158},
  number = {2},
  pages = {158-167},
  month = {06/2023},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_15_0.pdf},
  doi = {10.9781/ijimai.2021.10.010},
}