LAUSR

In conventional person re-identification (re-id), the images used for model training in the training probe set and training gallery set are all assumed to be instance-level samples. This labeling across multiple non-overlapping camera views from raw video surveillance is expensive and time-consuming. To overcome these issues, we consider a weakly supervised person re-id setting. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. To solve the weakly supervised person re-id problem, we develop deep graph metric learning (DGML). On the one hand, DGML measures the consistency between intra-video spatial graphs of consecutive frames. On the other hand, DGML distinguishes the inter-video spatial graphs captured from different camera views at different sites simultaneously. To further explicitly embed weak supervision into the DGML and solve the weakly supervised person re-id problem, we introduce weakly supervised regularization (WSR). We conduct extensive experiments to demonstrate the feasibility of the weakly supervised person re-id approach and its special cases (e.g. its bag-to-bag extension) and show that the proposed DGML is effective.