Efficient Video Summarization Using Principal Person Appearance for Video-Based Person Re-Identification

In video-based person re-identification, while most work has focused on problems of person signature representation and matching between different cameras, intra-sample variance is also a critical issue to be addressed. There are various factors that cause the intra-sample variance such as detection/tracking inconsistency, motion change and background. However, finding individual solutions for each factor is difficult and complicated. To deal with the problem collectively, we assume that it is more effective to represent a video with signatures based on a few of the most stable and representative features rather than extract from all video frames. In this work, we propose an efficient approach to summarize a video into a few of discriminative features given those challenges. Primarily, our algorithm learns principal person appearance over an entire video sequence, based on low-rank matrix recovery method. We design the optimizer considering temporal continuity of the person appearance as a constraint on the low-rank based manner. In addition, we introduce a simple but efficient method to represent a video as groups of similar frames using recovered principal appearance. Experimental results show that our algorithm combined with conventional matching methods outper-forms state-of-the-arts on publicly available datasets.