Improved Vehicle Classification in Long Traffic Video by Cooperating Tracker and Classifier Modules

Visual surveillance systems intend to extract meaning from a scene. Two initial steps for this extraction are the detection and tracking of objects followed by the classification of these objects. Often times these are viewed as separate problems where each is solved by an individual module. These tasks should not be done individually because they can help one another. This paper demonstrates the benefit gained both in tracking and classification through the communication between these individual modules. This is shown on a real-time system monitoring highway traffic. The system retreives online video at 10 frames/sec and conducts tracking and classification simultaneously. Results show an improvement from 74% to 88% accuracy in classification results.