Illumination and scale invariant relevant visual features with hypergraph-based learning for multi-shot person re-identification

Abstract

Person re-identification which aims at matching people across disjoint cameras has received increasing attention due to the widespread use of video surveillance applications. Existing methods concentrate either on robust feature extraction or view-invariant feature transformation. However, the extracted features suffer from various limitations such as color inconsistency and scale variations. Besides, during matching, a probe is compared against each gallery instance which represents only the pairwise relationship and ignores the high order relationship among them. To address these issues, we propose a multi-shot person re-identification framework that first performs a preprocessing task on images to address illumination variations for maintaining the color consistency. Subsequently, we formulate an approach to handle scale variations in the pedestrian appearances for keeping them with relatively a fixed scale ratio. Overlapped visual patches representing appearance cues are then extracted from the processed images. A structured multi-class feature selection approach is employed to select a set of relevant patches that simultaneously discriminates all distinct persons. These selected patches use a hypergraph to represent the visual association among a probe and gallery images. Finally, for matching, we formulate a hypergraph-based learning scheme, which considers both the pairwise and high-order association among the probe and gallery images. The hypergraph structure is then optimized to yield an improved similarity score for a probe against each gallery instance. The effectiveness of our proposed framework is validated on three public datasets and comparison with state-of-the-art methods shows the superior performance of our framework.