Robust method of changes of light to detect and track vehicles in traffic scenes

In this paper Intelligent image processing-based rapid method for detecting and tracking moving vehicles at intersections is proposed. In the detection part, the Gaussian mixture model used to obtain the moving parts. Then, the targets were detected using HOG features extracted from training images, Adaboost Cascade Classifier and the trained SVM.
At the tracking part, a number of key points on the image of the vehicle were identified at first. The center of mass of the object and the edges were used to obtain these key points because these points are primarily important and more common in tracking rigid bodies. Then, these points were tracked in consecutive frames using definitive adaptive procedures. Also, the Kalman filter was used to estimate new locations when the detector was not able to detect the targets.
The major advantage of this method compared to previous methods, is resistance against vehicle's overlapping and changes in Illuminations,so that the detection accuracy is 90.80% on overloaded traffic scenes and 88.75% accuracy on the tracking vehicles.