Multi-shot Person Re-identification with Automatic Ambiguity Inference and Removal

This work tackles the challenging problem of multi-shot person re-identification in realistic unconstrained scenarios. While most previous research within re-identification field is based on single-shot mode due to the constraint of scales of conventional datasets, multi-shot case provides a more natural way for person recognition in surveillance systems. Multiple frames can be easily captured in a camera network, thus more complementary information can be extracted for a more robust signature. To re-identify targets in real world, a key issue named identity ambiguity that commonly occurs must be solved preferentially, which is not considered by most previous studies. During the offline stage, we train an ambiguity classifier based on the shape context extracted from foreground responses in videos. Given a probe pedestrian, this paper employs the offline trained classifier to recognize and remove ambiguous samples, and then utilizes an improved hierarchical appearance representation to match humans between multiple-shots. Evaluations of this approach are conducted on two challenging real-world datasets, both of which are newly released in this paper, and yield impressive performance.