A Generalized Optimization Framework for Score Aggregation in Person Re-identification Systems

Person re-identification is the problem of identifying a person over multiple cameras in video-based surveillance. In this paper, we propose a novel generalized optimization framework for combining results from different methods for person re-identification to significantly improve re-identification rates. The proposed framework evaluates the similarity of score distributions by means of Bhattacharyya distance to arrive at an optimum solution that minimizes a defined cost function. Using our framework, we employ similarity scores from existing algorithms to generate score aggregates, which are then used for ranking the gallery images based on their "closeness" to a given probe image. The optimization problem is solved using Genetic Algorithm, a heuristic optimization algorithm. Our results show significant improvement in performance for person re-identification using existing algorithms on challenging datasets - VIPeR, CUHK01, CUHK03 and QMUL-GRID.