Intelligent Surveillance System Using Deep Learning

Vishwajit Dandage¹ , Radhika Mahore² , Hiemanshu Gautam³ , Akshay Ghavale⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.6 , pp.19-21, Dec-2020

Online published on Dec 31, 2020

Copyright © Vishwajit Dandage, Radhika Mahore, Hiemanshu Gautam, Akshay Ghavale . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
It`s of intensive importance to develop a way for automatic surveillance video analysis to acknowledge the presence of violence. During this work, to identify violent videos, we recommend a deep neural network. A convolutional neural network is used with a pre-trained inception model for extracting frame level features from a video. The characteristics of the frame level are then collectively employed during a long remembering variant that uses fully connected layers and leaky rectified linear units. Alongside the long remembering, the convolutional neural network is capable of capturing localized spatio-temporal features that alter the analysis of native motion within the video. The performance is more evaluated in terms of accuracy of recognition on standard benchmark datasets. The approach planned outperforms state-of -the-art strategies whereas process the videos in real time.

Key-Words / Index Term :
CNN; LSTM; RNN; Inception; GoogLeNet; VGG; AlexNet; Data-Set; Deep Learning; TensorFlow

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