Versatile Defacement Detection by Monitoring Video Sequences Using Deep Learning

Newlin Shebiah R and Arivazhagan S
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Newlin Shebiah R: Centre for Image Processing and Pattern Recognition, Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India
Arivazhagan S: Centre for Image Processing and Pattern Recognition, Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India

European Journal of Engineering and Technology Research, 2019, vol. 4, issue 7, 37-41

Abstract: The main objective of this paper is to detect vandal and vandalism by monitoring recorded video sequences. Vandalism is one of the most commonly occurring crimes in the society that indirectly affects the economy of the country. The proposed algorithm takes in the input from the video extracted from surveillance camera which prevails in public places. Further, it is converted into frames and subtracted with the background to detect the foreground object. The background subtracted image contains both human and non-human moving objects. In order to differentiate human pixels and other moving objects in the video sequence, discriminative features are extracted using deep architecture and classified using SVM classifier. Deep features proved to be highly discriminative when compared with the handcrafted Histogram of Oriented Gradients features. By analyzing the dwell time of the person in the restricted scene and his motion pattern with time and significant change in background vandalism act is declared and the person is considered as vandal. The proposed method was evaluated on the videos collected from You Tube with the contents taken during night time, multiple vandals, car vandals etc.

Keywords: Vandalism; Background Subtraction; Feature Extraction; SVM Classifier; Alexnet (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:4:y:2019:i:7:id:61396

DOI: 10.24018/ejeng.2019.4.7.1396

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