Improving person re-identification systems: a novel score fusion framework for rank-n recognition

Person re-identification is an essential technique for video surveillance applications. Most existing algorithms for person re-identification deal with feature extraction, metric learning or a combination of both. Combining successful state-of-the-art methods using score fusion from the perspective of person re-identification has not yet been widely explored. In this paper, we endeavor to boost the performance of existing systems by combining them using a novel score fusion framework which requires no training or dataset-dependent tuning of parameters. We develop a robust and efficient method called Unsupervised Posterior Probability-based Score Fusion (UPPSF) for combination of raw scores obtained from multiple existing person re-identification algorithms in order to achieve superior recognition rates. We propose two novel generalized linear models for estimating the posterior probabilities of a given probe image matching each of the gallery images. Normalization and combination of these posterior probability values computed from each of the existing algorithms individually, yields a set of unified scores, which is then used for ranking the gallery images. Our score fusion framework is inherently capable of dealing with different ranges and distributions of matching scores emanating from existing algorithms, without requiring any prior knowledge about the algorithms themselves, effectively treating them as "black-box" methods. Experiments on widely-used challenging datasets like VIPeR, CUHK01, CUHK03, ETHZ1 and ETHZ2 demonstrate the efficiency of UPPSF in combining multiple algorithms at the score level.