A Comparative Study On Machine Learning Algorithms Using Hog Features For Vehicle Tracking And Detection
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Abstract
Vehicle detection and tracking are important in applications such as highway traffic surveillance, management, and urban traffic planning. For vehicle tracking, to find average speed of each individual vehicle, and vehicle categorising targets, a road-based vehicle detection system is utilized. In intelligent surveillance systems (ITSs), traffic surveillance is now a great concern. Video-based monitoring systems have made great changes in traffic surveillance due to developments in computer vision. The main aim of this project is to use Histogram of oriented gradients (HOG) feature extraction algorithm to identify multiple vehicles in images and then classify them using various classification techniques
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