基于图模型与加权损失策略的视频行人重识别研究

针对监控视频中行人外观、姿态相似等现象导致的视频行人重识别准确率低的问题进行了研究，提出了一种基于图模型的视频行人重识别方法，有效利用了视频中的时序信息，实现跨帧及帧内区域的信息交互。具体来说，利用跨帧分块区域间的关联信息建立区域节点间的固有关系，并进行特征传播迭代更新区域信息。另一方面，在度量学习过程中，提出了一种加权损失函数策略，这个方法将先前挖掘策略中的二进制分配法(即丢弃或保留该样本)优化为连续分数分配法，解决了可用样本未被有效利用的问题。将模型在MARS和DukeMTMC-VideoReID两个数据集上进行了评估，实验结果证实了提出方法的有效性。

Video-based person re-identification based on graph model and weighted loss strategy

Aiming at the problem of low person re-identification accuracy caused by similar appearance and posture of person in surveillance videos, this paper proposed a video-based person re-identification method based on a graph model, which effectively utilized the time sequence information in the video to realize the information interaction across frames and intra-frame regions. Specifically, it used the correlation information between the cross-frame block regions to establish the inherent relationship between the regional nodes, and iteratively updated the regional information through feature propagation. On the other hand, in the metric learning process, it proposed a weighted loss function strategy, which optimized the binary allocation method(that is, discarding or retaining the sample) in the previous mining strategy into a continuous score allocation method, which solved the problem that the available samples were not used efficiently. Finally, it evaluated the model on MARS and DukeMTMC-VideoReID datasets, and the experimental results confirm the effectiveness of their proposed method.