Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras
This paper proposes a novel unsupervised algorithm learning discriminative features in the context of matching road vehicles between two nonoverlapping cameras. The matching problem is formulated as a same-different classification problem, which aims to compute the probability of vehicle images from two distinct cameras being from the same vehicle or different vehicle(s). We employ a novel measurement vector that 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 measure is determined by an unsupervised learning algorithm that optimally separates the same-different classes in the combined measurement space. This is achieved with a weak classification algorithm that automatically collects representative samples from same-different classes, followed by a more discriminative classifier based on Fisher's Linear Discriminants and Gibbs Sampling. The robustness of the match measures and the use of unsupervised discriminant analysis in the classification ensures that the proposed method performs consistently in the presence of missing/false features, temporally and spatially changing illumination conditions and systematic misalignment caused by different camera configurations. Extensive experiments based on real data of more than 200 vehicles at different times of the day demonstrate promising results.
unsupervised learning vehicle matching nonoverlapping cameras fisher's linear discriminants gibbs sampling object recognition edge feature object reacquisition