Good practices on building effective CNN baseline model for person re-identification

Fu Xiong; Yang Xiao; Zhiguo Cao; Kaicheng Gong; Zhiwen Fang; Joey Tianyi Zhou

doi:10.1117/12.2524386

6 May 2019 Good practices on building effective CNN baseline model for person re-identification

Fu Xiong, Yang Xiao, Zhiguo Cao, Kaicheng Gong, Zhiwen Fang, Joey Tianyi Zhou

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110690I (2019) https://doi.org/10.1117/12.2524386
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

Person re-identification is indeed a challenging visual recognition task due to the critical issues of human pose variation, human body occlusion, camera view variation, etc. To address this, most of the state-of-the-art approaches are proposed based on deep convolutional neural network (CNN), being leveraged by its strong feature learning power and classification boundary fitting capacity. Although the vital role towards person re-identification, how to build effective CNN baseline model has not been well studied yet. To answer this open question, we propose 3 good practices in this paper from the perspectives of adjusting CNN architecture and training procedure. In particular, they are adding batch normalization after the global pooling layer, executing identity categorization directly using only one fully-connected layer, and using Adam as optimizer. The extensive experiments on 3 widely-used benchmark datasets demonstrate that, our propositions essentially facilitate the CNN baseline model to achieve the state-of-the-art performance without any other high-level domain knowledge or low-level technical trick.

Citation Download Citation

Fu Xiong, Yang Xiao, Zhiguo Cao, Kaicheng Gong, Zhiwen Fang, and Joey Tianyi Zhou "Good practices on building effective CNN baseline model for person re-identification", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690I (6 May 2019); https://doi.org/10.1117/12.2524386

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