Vehicle Detection and Tracking Using Kalman Filter Over Aerial Images

Abstract

For intelligent transportation systems (ITSs) and planning that makes use of exact location intelligence, accurate vehicle classification, and tracking are topics that are becoming more and more vital. This paper presents a model for the detection and tracking of vehicles in roundabout aerial images. The detection is being done using a combination of blob detection and improved occlusion handling technique based on geometrical points of the vehicle model. The detected vehicles are assigned ID based on IoU matching, similarity matching, and centroid of the vehicle bounding box. The moving cars are then passed onto the tracking algorithm which implements the Kalman filter and vehicle re-identification methods. The trajectories of each detected vehicle are derived. The preciseness of the detection and tracking algorithms are 87% and 90% respectively. The experimental findings showed that the proposed detection and tracking model had consistent results for complex environments having heavy traffic flow conditions.