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  • Open Access


    Person Re-Identification Based on Joint Loss and Multiple Attention Mechanism

    Yong Li, Xipeng Wang*

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 563-573, 2021, DOI:10.32604/iasc.2021.017926

    Abstract Person re-identification (ReID) is the use of computer vision and machine learning techniques to determine whether the pedestrians in the two images under different cameras are the same person. It can also be regarded as a matching retrieval task for person targets in different scenes. The research focuses on how to obtain effective person features from images with occlusion, angle change, and target attitude change. Based on the present difficulties and challenges in ReID, the paper proposes a ReID method based on joint loss and multi-attention network. It improves the person re-identification algorithm based on global characteristics, introduces spatial attention… 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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