Publication Date: 2023/04/29
Abstract: In the modern era, usage of video surveillance has increased which in fact increase the size of data. Video surveillance is widely using in both public and private areas for improving the security and safety of human being. Hence, it is important to identify and analyse the video in different angle so as to extract the most important information from the video. The video may contain both usual or unusual event, mostly the users need to find out the unusual event from the video that may affect their security. To differentiate both the events separately, here we are considering a special scenario related with vehicle. The vehicles on road can move in different ways, where they can follow or violate traffic rules, illegal U-turns, accidents etc. In this paper, the unusual event considered is the accidents on the road. The technology used is deep learning and computer vision. The neural network selected is the DenseNet. The DenseNet is a convolutional neural network. The peculiarity of a DenseNet architecture is that each layer in a network is connected to every other layer. For each layer, the feature maps of all the preceding layers are used as inputs, and its own feature maps are used as input for each subsequent layer. The deployment of DenseNet along with computer vision increases the accuracy of the system.
Keywords: Deep Learning, Computer Vision, Segmentation, Tracking.
DOI: https://doi.org/10.5281/zenodo.7878739
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23APR1240.pdf
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