A Novel Approach for Real Time Multi-Scene Violent Activities Recognition with Modified ResNet50 and LSTM

A Novel Approach for Real Time Multi-Scene Violent Activities Recognition with Modified ResNet50 and LSTM

© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Devang Jani, Anand Mankodia
DOI : 10.14445/22315381/IJETT-V70I8P231

How to Cite?

Devang Jani, Anand Mankodia, "A Novel Approach for Real Time Multi-Scene Violent Activities Recognition with Modified ResNet50 and LSTM," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 292-309, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P231

Day by day, the demand for autonomous video surveillance systems has been escalating due to inefficient manual inspection power of identifying anomalies in recorded videos by human beings. Currently, most video surveillance systems use manual video inspection after detecting suspicious activities or trying to use manual inspection of videos once the complaint regarding anomalous, violent or suspicious activities is filed at a particular area or location. Implementation of the real-time scanning of the video stream from the multiple cameras at the central level and a single camera at the edge level is a very big challenge due to the requirement of GPU, computational hardware as well a large amount of computation power with different provocations for the mutual type of human abnormal activities behaviors. The proposed methods represented in this paper provide a novel idea about real-time recognition of nine different mutual violent actions and Normal nonviolent actions using modified deep learning models, namely ResNet50 in association with LSTM. The proposed method provides Nine different diversified violent activities, specifically Attacking, Fighting, hitting with an object, Kicking, Punching, Pushing, Shooting with a Gun, Slapping, stabbing with a knife and one Nonviolent activity that is Normal class. A total of ten violent & nonviolent classes with an accuracy of 87.60% were developed and tested using TensorFlow, Keras and Supercomputing facilities. The Proposed Method is unaccustomed to multi-class violent activity recognition in a real-time environment. Real-time violent activities recognition is a summons as the violent recognition algorithms available to date can provide the decision regarding the events that occurs in the video, whether violent or nonviolent. It can work in short-length recorded videos in a non-real-time environment, a post-effect type of processing that cannot prevent future violent activities as the proposed methods can. The design of the Multi-class Violent recognition model is an arduous and much more time-consuming task due to the performance of each class affecting the overall accuracy and efficiency of the model. It is also annoying and tiresome because it requires continuous time of some days without interruption for the model's training

Resnet50, LSTM, Violent and Nonviolent Classes, Surveillance System, Real-Time.

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