Open AccessAnchor-Free Detector and Re-Identification with Joint Loss for Person Search
Author(s) -
Ming Liu,
Laifeng Hu,
Yaog Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1678/1/012116
Subject(s) - computer science , detector , artificial intelligence , offset (computer science) , margin (machine learning) , panorama , task (project management) , matching (statistics) , identification (biology) , recall , recall rate , computer vision , pattern recognition (psychology) , machine learning , mathematics , engineering , telecommunications , botany , biology , linguistics , statistics , philosophy , systems engineering , programming language
Person search aims at matching a target person from a gallery of panorama images. Its performance depends on the localization accuracy and the recall rate of the pedestrian detector. FoveaBox detector which outperforms others is utilized in our framework. It generates high-quality region proposals for following re-identification (re-id). A joint loss function is proposed to train the network effectively. It is made up of on-the-fly Online Instance Matching (OIM) and proposal pair double margin contrastive (PPDMC) loss. We propose offset guided erasing instead of random erasing to solve the occlusion problem in person search task preferably. Experiments show that our method performs more effectively against the state-of-the-art methods on two widely used person search datasets.