Learning Enhanced 3D Models for Vehicle Tracking

This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic object-centred and appearance-based representations in computer vision giving improved hypothesis verification under iconic matching. The "appearance" of a 3D object is learnt using an eigenspace representation obtained as it is tracked through a scene. The feature representation implicitly models the background and the objects observed enabling the segmentation of the objects from the background. The method is shown to enhance model-based tracking, particularly in the presence of clutter and occlusion, and to provide a basis for identification. The unified approach is discussed in the context of the traffic surveillance domain. The approach is demonstrated on real-world image sequences and compared to previous (edge-based) iconic evaluation techniques.

• Type: Conference
• Year: 1998
• Where: BMVC
• Authors: James M. Ferryman, Anthony D. Worrall, Stephen J. Maybank
• Keywords: BMVC 1998, Computer Vision, Hypothesis Verification, Hypothesis Verification Strategy, Iconic Matching