Person re-identification by shifting-subclip

Because of the proliferation of surveillance camera and the wide range of its utilization, person re-identification (re-ID) technology has been drawing attention. Although video-based re-ID to extract spatial and temporal features has become the mainstream these days, the number of input frames for most of video-based re-ID architectures is usually fixed. Thus, frame looping or frame duplication is applied if the number of input frames is less than the specified value. However, by applying CNN to the entire looped frames, the temporal features cannot be well trained. Therefore, in this paper, we propose a method to apply CNN not to the entire looped frames but several times to the neighboring frames by shifting frames (shifting-subclip), then the average of each CNN output is processed for further temporal feature extraction. The evaluation using the MARS dataset shows that mAP (Mean Average Precision score) and CMC (Cumulative Match Curve) with the proposal is higher than that without it. It suggests that it is effective to apply the proposed shifting-subclip in the MARS dataset.