PoolNet deep feature based person re-identification
Pages 24967 - 24989
Abstract
Learning with Deep Neural Networks has recently reached state-of-the-art outcomes for Person Re-Identification. Effective learning can be accomplished only with efficient features robust to illumination and viewpoint changes. This paper proposes a new feature representation method called PoolNet Deep Feature (PNDF) for person re-identification with Convolution Neural Networks. The proposed CNN architecture called PoolNet consists of two Pool Added Blocks (PAB) and a Pool Concatenated Block (PCB) to extract the more sophisticated dominant and precise features for better learning towards a person’s re-identification. The efficiency of the proposed method is demonstrated in terms of re-identification accuracy by implementing it on the challenging small scale & large-scale person re-identification datasets such as VIPeR, Market1501, CUHK03, GRID, and LaST.
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Publication History
Published: 12 January 2023
Accepted: 02 January 2023
Revision received: 28 November 2022
Received: 08 June 2022
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