Abstract
This paper proposes a novel feature-fusion frame work for the image-to-video person re-identification. In this framework, we formulate person re-id problem as a classification-based information retrieval where a person appearance model is learned in the training phase and the identity of an interested person is determined by the probability that his/her probe image belongs to the model. To learn the person appearance model, two features that are Kernel descriptor (KDES) and Convolution Neural Network (CNN) are investigated. Then, three fusion schemes including early fusion, product rule and query-adaptive late fusions are proposed. Extensive experiments have been conducted on two public benchmark datasets: CAVIAR4REID and RAID. The obtained accuracies at rank 1 are 95.00% and 94.29% for CAVIAR4REID and RAID, respectively. Among three proposed fusion schemes, the two late fusion schemes obtain better results than that of the other one. They gain approximately 10% improvement for accuracy at Rank 1.
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This work has been supported by University of Transport and Communications under grant code: T2018-DT-007.
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Nguyen, TB., Le, TL., Nguyen, DD., Pham, DT. (2018). A Reliable Image-to-Video Person Re-identification Based on Feature Fusion. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_41
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