Video Person Re-ID: Fantastic Techniques and Where to Find Them

21 Nov 2019  ·  Priyank Pathak, Amir Erfan Eshratifar, Michael Gormish ·

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.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Person Re-Identification MARS B-BOT + OSM + CL Centers* (Re-rank) mAP 88.5 # 1
Person Re-Identification MARS B-BOT + Attention and CL loss Rank-1 88.6 # 8
Person Re-Identification MARS B-BOT + Attention and CL loss* mAP 82.9 # 10
Person Re-Identification PRID2011 B-BOT + Attention and CL loss* Rank-1 96.6 # 1

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