Efficient Data Association for Multiple Vehicles Tracking

Abstract

Data association problem has been an important issue for the multiple vehicles tracking in a vehicle tracking system. In this paper, we present an efficient data association method to track multiple vehicles in a sequence of traffic video frames. We first introduce the compact rectangular region-of-interest (crROI) that tightly encloses a vehicle and has the rotation-invariant property. The subsequent processing is based on the crROI instead of a vehicle image itself to avoid the processing overhead. Next, we extract the features from the crROI such as shape, size, and spatial relationship. Using these features, we define the similarity metric between two vehicles, and present the association method that matches a vehicle in a frame with the corresponding vehicle in its consecutive frame. An experimental result shows that the proposed method identifies and tracks vehicles effectively and efficiently in the curve or crossroad environment where multiple vehicles appear.