Unsupervised Learning of Discriminative Edge Measures for Vehicle Matching between Non-Overlapping Cameras

This paper proposes a novel method for matching road vehicles between two non-overlapping cameras. The matching problem is formulated as a same-different classification problem: probability of two observations from two distinct cameras being from the same vehicle or from different vehicles. We employ a novel measurement vector consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. The weight of each match measure in the final decision is determined by a novel unsupervised learning process so that the same-different classification can be optimally separated in the combined measurement space. The robustness of the match measures and the use of discriminant analysis in the classification ensures that the proposed method performs better than existing edge-based approaches, especially in the presence of missing/false edges caused by shadows and different illumination conditions, and systematic misalignment c...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Ying Shan, Harpreet S. Sawhney, Rakesh Kumar
• Keywords: Aligned Vehicle Edge, Associated Robust Measures, Computer Vision, CVPR 2005, Independent Edge-based Measures, Match Measures