Video Person Re-ID: Fantastic Techniques and Where to Find Them
This work addresses the problem of identifying individuals across camera views for security and surveillance applications, but it is incremental as it builds on existing attention-based methods.
The paper tackles video-based person re-identification by proposing a hybrid loss function combining attention, center, and Online Soft Mining loss, which achieves state-of-the-art results on MARS and PRID 2011 datasets.
The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github.