Abstract
Baggage re-identification (ReID) is a particular and crucial object ReID task. It aims to only use the baggage image data captured by the camera to complete the cross-camera recognition of baggage, which is of great value to security inspection. Two significant challenges in the baggage ReID task are broad view differences and distinct cross-domain characteristics between probe and gallery images. To overcome these two challenges, we propose MVAD-Net, which aims to learn view-aware and domain-invariant representation for baggage ReID by multi-view attention and domain-invariant learning. The experiment shows that our network has achieved good results and reached an advanced level while consuming minimal extra cost.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. U20B2062), the fellowship of China Postdoctoral Science Foundation (No. 2021M690354), the Beijing Municipal Science & Technology Project (No. Z191100007419001).
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Zhao, Q., Ma, H., Lu, R., Chen, Y., Li, D. (2021). MVAD-Net: Learning View-Aware and Domain-Invariant Representation for Baggage Re-identification. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_12
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