Registration of Challenging Image Pairs: Initialization, Estimation, and Decision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Object matching in disjoint cameras using a color transfer approach
Machine Vision and Applications
Multiple cue integrated action detection
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Incremental unsupervised three-dimensional vehicle model learning from video
IEEE Transactions on Intelligent Transportation Systems
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
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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 caused by different camera configurations. Extensive experiments based on real data of over 200 vehicles at different times of day demonstrate promising results.