刘寒松.基于关系融合和特征分解的车辆重识别算法[J].光电子激光,2024,35(7):708~715
基于关系融合和特征分解的车辆重识别算法
Vehicle re-identification algorithm based on relationship fusion and feature decomposition
投稿时间:2023-05-27  修订日期:2023-08-31
DOI:
中文关键词:  车辆重识别(Re-ID)  关系融合  特征分解  注意力机制
英文关键词:vehicle re-identification (Re-ID)  relation fusion  feature decomposition  attention mechanism
基金项目:山东省科技型中小企业创新能力提升工程项目(2021TSGC1030)资助项目
作者单位
刘寒松 中国社会科学院大学北京 102401
松立控股集团股份有限公司山东 青岛 266000 
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中文摘要:
      在车辆重识别(re-identification,Re-ID) 任务中,通过对全局及局部信息的联合提取已成为目前主流的方法,是许多重识别模型在提取局部信息时只关注了丰富程度而忽略了完整性。针对该问题,提出了一种基于关系融合和特征分解的算法。该算法从空间与通道维度出发,设计对骨干网络所提取的特征沿垂直、水平、通道3维度分割,首先,为了更好地凸显车辆的前景区域,提出一种混合注意力模块(mixed attention module,MAM) ,之后,为了在空间维度上挖掘丰富特征信息的同时使得网络关注更完整的感兴趣区域,设计对垂直及水平方向的分割后的特征实现基于图的关系融合。为了赋予网络捕捉更具判别性信息的能力,在通道方向上对分割后的局部特征实现特征分解。最后,在全局分支特征与局部分支下所提取的鲁棒性特征的共同作用下实现车辆重识别。实验结果表明,本文算法在两个主流车辆重识别数据集上取得了更先进的性能。
英文摘要:
      In the task of vehicle re-identification (Re-ID),joint extraction of global and local information has become the mainstream approach.However,many Re-ID models only focus on the richness of local information while neglecting completeness.To address this issue,an algorithm based on relationship fusion and feature decomposition is proposed in this paper.The algorithm starts from the spatial and channel dimensions,dividing the features extracted by the backbone network along the vertical,horizontal,and channel dimensions.Firstly,to better highlight the foreground region of the vehicle,a mixed attention module (MAM) is proposed.Then,to explore rich feature information in the spatial dimension while making the network pay attention to more complete regions of interest,graph-based relation fusion is designed for the segmented features in the vertical and horizontal directions.To endow the network with the ability to capture more discriminative information,feature decomposition is implemented on the segmented local features in the channel direction.Finally,vehicle Re-ID is achieved through the joint effect of the features extracted from the global branch and the robust features from the local branches.Experimental results demonstrate that the proposed algorithm achieves state-of-the-art performance on two popular vehicle Re-ID datasets.
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