Open AccessA G2G Similarity Guided Pedestrian Re-identification Algorithm
Author(s) -
Guangcai Wang,
Gao Shang,
Di Fan
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 - 10.1088/1742-6596/1453/1/012035
Subject(s) - similarity (geometry) , pedestrian , computer science , artificial intelligence , identification (biology) , process (computing) , matching (statistics) , key (lock) , pattern recognition (psychology) , computer vision , image (mathematics) , field (mathematics) , image retrieval , mathematics , engineering , botany , transport engineering , biology , statistics , computer security , pure mathematics , operating system
Pedestrian re-identification aims to settle the matching problem for a given target pedestrian under the multi-camera with non-overlapping visual field, so as to achieve the goal of pedestrian retrieval. In this paper, the similarity between the gallery images (G2G similarity) will be used to guide and refine the similarity between query images and gallery images (P2G similarity). It is also introduced into the training process and playing a supervisory role. To fully utilize details of the images, the image features are horizontally overlapped into groups, and the similarities between each group of the query images and the gallery images are calculated respectively. By learning the weights of grouping features in the training process, the importance of key parts can be automatically perceived. The results on the Market-1501 and CUHK03 datasets prove the effectiveness of proposed algorithm.