ITM Web Conf.
Volume 29, 20191st International Conference on Computational Methods and Applications in Engineering (ICCMAE 2018)
|Number of page(s)||8|
|Section||Applications in Information Technologies|
|Published online||15 October 2019|
Deep Learning Approach for Violence Detection in Urban Areas
Politehnica University of Timisoara, Department of Computers and Information Technology,
Bd. V.Parvan no. 2,
2 Politehnica University of Timisoara, Department of Electronics, Telecommunications, and Information Technology, Bd. V. Parvan no. 2 Timisoara, Romania
1 Corresponding author: firstname.lastname@example.org
Today modern cities tend to grow rapidly. The increased population density brings new challenges in term of public safety. Crime and violence are hard to be detected and managed especially in specific crowd environments like music concerts, sport events or public meetings. To overcome this issue the city administration should implement monitoring systems capable of detecting and analysing such situations. The work presented here combines two approaches that enable implementation of an efficient solution adapted for this purpose. The first one involves sensor networks that prove to be cost effective solution in a smart city environment. They can benefit on the existing surveillance infrastructure and allows rapid deployment. The second approach uses deep learning techniques. They demonstrate outstanding performances in image and actions classification based on a prior learning process. By combining these two approaches we succeed to obtain a real-time and cost-effective solution designed for urban area surveillance networks.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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