Cascade Transformers for End-to-End Person Search

• URL: http://arxiv.org/abs/2203.09642v1
• Date: Thu, 17 Mar 2022 22:42:12 GMT
• Title: Cascade Transformers for End-to-End Person Search
• Authors: Rui Yu, Dawei Du, Rodney LaLonde, Daniel Davila, Christopher Funk, Anthony Hoogs, Brian Clipp
• Abstract summary: We propose the Cascade Occluded Attention Transformer (COAT) for end-to-end person search. COAT focuses on detecting people in the first stage, while later stages simultaneously and progressively refine the representation for person detection and re-identification. We demonstrate the benefits of our method by achieving state-of-the-art performance on two benchmark datasets.
• Score: 18.806369852341334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of person search is to localize a target person from a gallery set of scene images, which is extremely challenging due to large scale variations, pose/viewpoint changes, and occlusions. In this paper, we propose the Cascade Occluded Attention Transformer (COAT) for end-to-end person search. Our three-stage cascade design focuses on detecting people in the first stage, while later stages simultaneously and progressively refine the representation for person detection and re-identification. At each stage the occluded attention transformer applies tighter intersection over union thresholds, forcing the network to learn coarse-to-fine pose/scale invariant features. Meanwhile, we calculate each detection's occluded attention to differentiate a person's tokens from other people or the background. In this way, we simulate the effect of other objects occluding a person of interest at the token-level. Through comprehensive experiments, we demonstrate the benefits of our method by achieving state-of-the-art performance on two benchmark datasets.

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