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  • Open Access


    A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

    Wei Sun1,2,*, Xuan Chen3, Xiaorui Zhang1,3, Guangzhao Dai2, Pengshuai Chang2, Xiaozheng He4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3549-3561, 2021, DOI:10.32604/cmc.2021.021627

    Abstract Vehicle re-identification (ReID) aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario. It has gradually become a core technology of intelligent transportation system. Most existing vehicle re-identification models adopt the joint learning of global and local features. However, they directly use the extracted global features, resulting in insufficient feature expression. Moreover, local features are primarily obtained through advanced annotation and complex attention mechanisms, which require additional costs. To solve this issue, a multi-feature learning model with enhanced local attention for vehicle re-identification (MFELA) is proposed in this paper.… More >

  • Open Access


    Vehicle Re-Identification Model Based on Optimized DenseNet121 with Joint Loss

    Xiaorui Zhang1,2,*, Xuan Chen1, Wei Sun2, Xiaozheng He3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3933-3948, 2021, DOI:10.32604/cmc.2021.016560

    Abstract With the increasing application of surveillance cameras, vehicle re-identification (Re-ID) has attracted more attention in the field of public security. Vehicle Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar appearances. Plentiful existing methods focus on local attributes by marking local locations. However, these methods require additional annotations, resulting in complex algorithms and insufferable computation time. To cope with these challenges, this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint loss. This model applies the SE block to automatically… More >

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