TY - JOUR
KW - Volume Crime Classification
KW - Crime Detection
KW - Malicious Activity Detection
KW - Deep Learning
AU - Atif Jan
AU - Gul Muhammad Khan
AB - 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%.
IS - Regular Issue
M1 - 2
N2 - 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%.
PY - 2023
SE - 158
SP - 158
EP - 167
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Real World Anomalous Scene Detection and Classification using Multilayer Deep Neural Networks
UR - https://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_15_0.pdf
VL - 8
SN - 1989-1660
ER -