Abstract
In the field of crossing-scene pedestrian identification, the recognition accuracy is low due to the large local variation of the samples. A method based on twice Feature-Aggregation-Separation (FAS) is proposed in this paper. Firstly, a novel network structure aggregating the same types and separating different types of features twice respectively is proposed. Secondly, a method of cross-input neighborhood differences is applied to deal with the features produced by the first aggregation-separation, and the results are taken as the input of the second aggregation-separation. Finally, the features produced by twice FAS are chosen for splicing, and the results are used for Softmax classifier. Compared with MCPB-TC [8] method based on features aggregation-separation, the proposed scheme can provide directional aggregation-separation of positive samples and negative samples. Compared with AIDLA [4] based on cross-input neighborhood differences, it offers better ability of discriminating inter-class and aggregating intra-class. It also outperforms those methods by the tests of CUHK01 and VIPeR data set.
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Acknowledgment
This work was supported by the 2016 Guangxi Science and Technology support program under Grant No. AB16380264 and 2016 Key Laboratory of Cognitive Radio and Information Processing (Guilin University of Electronic Technology), Ministry of Education Fund Project, Project No. CRKL160102.
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Chen, Y., Cai, X., Zeng, Y., Wang, M. (2017). Crossing-Scene Pedestrian Identification Method Based on Twice FAS. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_41
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DOI: https://doi.org/10.1007/978-981-10-6388-6_41
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