Real-Time Detection of Crime and Violence in Video Surveillance using Deep Learning

DOI

10.2991/978-94-6463-196-8_33How to use a DOI?

Keywords

Crime detection; Violence detection; DenseNet; LSTM; Abnormal detection; Blockchain; InceptionV3; fight detection

Abstract

Since its widespread application, deep learning is a vital part of the machine learning community's toolbox. With so many crimes and wrongdoings going on without adequate oversight in public spaces, various ways have been developed to identify crime and violence in the camera footage. Automated violence detection has become more important in video surveillance research. However, they have several restrictions, and much of the time, it is based on a certain set of circumstances. This research presents a 3D convolutional neural network-based technique for detecting violence in videos. Accuracy is improved by employing machine and deep learning techniques in a suggested manner. Performance evaluations have shown that the suggested technique effectively identifies violence in video clips. ‘ Experimental data show that the proposed strategy outperforms existing methods for identifying crimes and violence in films. The pre-training models, Inception-V3, InceptionResNetV2, ViolenceNet, and ViolenceNet-OF, were trained on the four datasets. The classification results on the validation data for each model are as follows: ViolenceNet OF 99.40%, InceptionResNetV2 89%, ViolenceNet pseudo 96, and InceptionV3 92%. The DenseNet model was chosen for our application system because it concatenates the feature maps in a simpler method than Inception or InceptionResNet, which are more complicated. It has a more durable design that requires fewer filters and settings to attain high efficiency than other models and achieves the highest accuracy.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ali Mansour Al-Madani
AU  - Vivek Mahale
AU  - Ashok T. Gaikwad
PY  - 2023
DA  - 2023/08/10
TI  - Real-Time Detection of Crime and Violence in Video Surveillance using Deep Learning
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 431
EP  - 441
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_33
DO  - 10.2991/978-94-6463-196-8_33
ID  - Al-Madani2023
ER  -