Abstract
Existing methods of vehicle re-identification (ReID) focus on training robust models on the fixed data while ignore the diversity in the training data, which limits generalization ability of the models. In this paper, it proposes an occlusion based discriminative feature mining (ODFM) method for vehicle re-identification, which increases the diversity of the training set by synthesizing occlusion samples, to simulate the occlusion problem in the real scene. To better train the ReID model on the data with large occlusions, an attention mechanism was introduced in the mainstream network to learn the discriminative features for vehicle images. Experimental results on two public ReID datasets, VeRi-776 and VehicleID verify the effectiveness of the proposed method comparing to the state-of-the-art methods.
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Acknowledgement
This research is supported in part by the National Natural Science Foundation of China (Nos. 61976002), Hainan Provincial Natural Science Foundation (Grant No. 117063), the Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2019A0033), and the National Laboratory of Pattern Recognition (NLPR) (201900046).
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Lin, X., Peng, S., Ma, Z., Zhou, X., Zheng, A. (2020). Occlusion Based Discriminative Feature Mining for Vehicle Re-identification. In: Qin, P., Wang, H., Sun, G., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1258. Springer, Singapore. https://doi.org/10.1007/978-981-15-7984-4_19
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DOI: https://doi.org/10.1007/978-981-15-7984-4_19
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