Model-Based Vehicle Localization Based on 3-D Constrained Multiple-Kernel Tracking

In this paper, we propose a novel model-based vehicle localization approach on the basis of surveillance cameras. The proposed approach regards each patch of the 3-D vehicle model as a kernel, and tracks the kernels under certain constrains facilitated by the 3-D geometry of the vehicle model. Meanwhile, a kernel density estimator is designed to well fit the 3-D vehicle model during tracking. With elegant application of the constrained multiple-kernel tracking facilitated with the 3-D vehicle model, the vehicles are able to be tracked efficiently and located precisely. The proposed approach achieves high effectiveness in the tracking and localization by taking advantage of the color similarity and shape fitness. Experimental results have shown the favorable performance of the proposed approach, in several scenarios, which efficiently tracks vehicles while maintaining the knowledge of 3-D geometry of the tracked vehicles.