Abstract
With the rapid development of urban transportation, vehicle re-identification has become a focal point in traffic management and vehicle tracking problems. In order to address the problem of small inter-class similarity among vehicles, previous studies utilize vehicle parsing models to extract local features. Therefore, we introduce the Squeeze-and-Excitation attention mechanism to extract important discriminative information from these local features. Furthermore, we propose a local co-occurrence attention mechanism to represent the proportion of common parts feature matching. To address the issue of large intra-class differences caused by vehicle direction change, we propose a lightweight and effective direction weighted fusion strategy. Experiments on two large datasets show that the proposed algorithm performs competitively.
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Acknowledgment
This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (No. 62006070), and partly supported by Key Scientific and Technological Project of Henan Province of China (Nos. 222102210197,222102210204,232102211013 and 222102210238).
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Zhou, Y., Yuan, C., Su, C., Zou, M., Zhu, X., Liang, W. (2024). Partial Attention-Based Direction-Aware Vehicle Re-identification. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Computer Science and Technology. ICCSE 2023. Communications in Computer and Information Science, vol 2023. Springer, Singapore. https://doi.org/10.1007/978-981-97-0730-0_15
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DOI: https://doi.org/10.1007/978-981-97-0730-0_15
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