Making Person Search Enjoy the Merits of Person Re-identification
Chuang Liu, Hua Yang, Qin Zhou, Shibao Zheng

TL;DR
This paper introduces TDN, a novel framework that leverages advanced Re-ID models to enhance one-step person search by disentangling subtasks, transferring knowledge, and exploiting context for improved accuracy.
Contribution
The paper proposes TDN, a framework that significantly boosts person search performance by integrating Re-ID knowledge transfer and context exploitation in a unified model.
Findings
TDN outperforms existing methods on public datasets.
Knowledge transfer from Re-ID models improves search accuracy.
Context-based ranking enhances retrieval results.
Abstract
Person search is an extended task of person re-identification (Re-ID). However, most existing one-step person search works have not studied how to employ existing advanced Re-ID models to boost the one-step person search performance due to the integration of person detection and Re-ID. To address this issue, we propose a faster and stronger one-step person search framework, the Teacher-guided Disentangling Networks (TDN), to make the one-step person search enjoy the merits of the existing Re-ID researches. The proposed TDN can significantly boost the person search performance by transferring the advanced person Re-ID knowledge to the person search model. In the proposed TDN, for better knowledge transfer from the Re-ID teacher model to the one-step person search model, we design a strong one-step person search base framework by partially disentangling the two subtasks. Besides, we…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Face recognition and analysis · Gait Recognition and Analysis
MethodsTemporaral Difference Network
