Vehicle Tracking for Urban Surveillance

Tracking is widely used in a variety of computer vision applications, ranging from video surveillance to medical imaging. The principal goal of tracking is to first identify regions of interest in a scene, and to then monitor the movements or changes of the object through the image sequence. In this paper, we focus on unsupervised vehicle tracking for low resolution aerial images taken from an urban area. Various optical effects have traditionally made this tracking problem very challenging. Objects are often lost in tracking due to intensity changes that result from shadowed or partially occluded regions of an image. Additionally, the presence of multiple vehicles in a scene can lead to mistakes in tracking and significantly increased computation time. We propose a featurebased tracking algorithm herein that will seek to mitigate these limitations. To first isolate vehicles in the initial frame, we apply three-frame change detection to the registered images. Feature points are identified in the labelled regions using the Harris corner criteria. To track a feature point from one frame to the next, we search for the point around a predicted location, determined from the feature’s previous motion, that minimizes the sum-of-squared-differences value. Finally, during the course of the image sequence, our algorithm constantly searches for new objects that might have entered the scene. We will demonstrate the success of our tracking approach through experimental considerations.