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3 months ago

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

Priyank Pathak Amir Erfan Eshratifar Michael Gormish

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

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.

Code Repositories

Benchmarks

BenchmarkMethodologyMetrics
person-re-identification-on-marsB-BOT + Attention and CL loss*
mAP: 82.9
person-re-identification-on-marsB-BOT + Attention and CL loss
Rank-1: 88.6
person-re-identification-on-marsB-BOT + OSM + CL Centers* (Re-rank)
mAP: 88.5
person-re-identification-on-prid2011B-BOT + Attention and CL loss*
Rank-1: 96.6

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