Video Person Re-ID: Fantastic Techniques and Where to Find Them
2019-11-21Code Available1· sign in to hype
Priyank Pathak, Amir Erfan Eshratifar, Michael Gormish
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ReproduceCode
- github.com/ppriyank/Video-Person-Re-ID-Fantastic-Techniques-and-Where-to-Find-ThemOfficialIn paperpytorch★ 63
Abstract
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| MARS | B-BOT + OSM + CL Centers* (Re-rank) | mAP | 88.5 | — | Unverified |
| MARS | B-BOT + Attention and CL loss* | mAP | 82.9 | — | Unverified |
| MARS | B-BOT + Attention and CL loss | Rank-1 | 88.6 | — | Unverified |
| PRID2011 | B-BOT + Attention and CL loss* | Rank-1 | 96.6 | — | Unverified |