Computer Vision-Based Vehicle Detection and Tracking

Abstract

To briskly detect people and vehicle on the road in a video sequence is a challenging problem. Most researches focus on detecting or tracking of specific targets only. Detection and tracking of vehicles have a great demand in video surveillance and traffic management applications. Each and every vehicle in the scene must be observed, while dealing with the traffic scenarios, which solves the problem occurred due to the traffic density in an area, is high due to occlusion caused by the large number of vehicles being observed. This paper portrays the computer vision-based vehicle detection and tracking for real-time scenarios. The proposed algorithm uses the blob analysis and tracking based on a correlation filter. Here, HOG is used for feature extraction, improvised correlation filter is used for tracking and AdaBoost classifier used for classifying the vehicle. The proposed system successfully tracks and counts the vehicles during and after occlusion with other vehicles which is shown in the result.