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ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) by Zhong, Xian (author);Liu, Yiting (author);Huang, Wenxin (author);Wang, Xiao (author);Ma, Bo (author);Yuan, Jingling (author) is a nonfiction available to read on EtoBox.

What is ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) about?

Person search is a significant computer vision task that requires addressing person detection and re-identification simultaneously. Body parts are frequently misaligned due to variation poses, occlusions, and partial missing, leading to the unsatisfied results of person search. Existing methods usually extract local features from the human body by the key point information, that cannot tackle the recognition task between a pair of persons with different body parts due to misalignment. Moreover, these methods overlook background information (e.g. the carries and the background reference object) which can also supplement effective features for representing the person. In this paper, we propose a part-aligned network with background (PANB) to address this misalignment issue. To learn local fine-grained features of different body parts, we fine-tune a parsing network to divide the body region into seven parts. In particular, our proposed method considers extracting the background features as the eighth part features to extract more robust representations, which is more rational and efficient. Furthermore, we design a reconstruction method to align the parts existing in both the query i

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Author

Zhong, Xian (author);Liu, Yiting (author);Huang, Wenxin (author);Wang, Xiao (author);Ma, Bo (author);Yuan, Jingling (author)

Publisher

IEEE

Published

2021

Language

EN

Category

nonfiction