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DOI: 10.14569/IJACSA.2020.0110285

Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

Author 1: Umair Muneer Butt
Author 2: Sukumar Letchmunan
Author 3: Fadratul Hafinaz Hassan
Author 4: Sultan Zia
Author 5: Anees Baqir

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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 2, 2020.

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Abstract: The meteoric growth of data over the internet from the last few years has created a challenge of mining and extracting useful patterns from a large dataset. In recent years, the growth of digital libraries and video databases makes it more challenging and important to extract useful information from raw data to prevent and detect the crimes from the database automatically. Street crime snatching and theft detection is the major challenge in video mining. The main target is to select features/objects which usually occurs at the time of snatching. The number of moving targets imitates the performance, speed and amount of motion in the anomalous video. The dataset used in this paper is Snatch 101; the videos in the dataset are further divided into frames. The frames are labelled and segmented for training. We applied the VGG19 Convolutional Neural Network architecture algorithm and extracted the features of objects and compared them with original video features and objects. The main contribution of our research is to create frames from the videos and then label the objects. The objects are selected from frames where we can detect anomalous activities. The proposed system is never used before for crime prediction, and it is computationally efficient and effective as compared to state-of-the-art systems. The proposed system outperformed with 81 % accuracy as compared to state-of-the-art systems.

Keywords: Anomalous detection; surveillance video; VGG16; VGG19; ConvoNet; AlexNet

Umair Muneer Butt, Sukumar Letchmunan, Fadratul Hafinaz Hassan, Sultan Zia and Anees Baqir, “Detecting Video Surveillance Using VGG19 Convolutional Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 11(2), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110285

@article{Butt2020,
title = {Detecting Video Surveillance Using VGG19 Convolutional Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110285},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110285},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {2},
author = {Umair Muneer Butt and Sukumar Letchmunan and Fadratul Hafinaz Hassan and Sultan Zia and Anees Baqir}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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