基于时空正则化的视频序列中行人的再识别

由于现实复杂情况中各种因素的干扰，行人再识别的过程中可能出现识别错误等问题。为了提高行人再识别的准确性，提出了一种基于时空正则化的行人再识别算法。首先，利用ResNet-50网络对输入的视频序列逐帧进行特征提取，将一系列帧级特征输入到时空正则化网络并产生对应的权重分数；然后，对帧级特征使用加权平均得到视频序列级特征，为避免权重分数聚集在一帧，使用帧级正则化来限制帧间差异；最后，通过最小化损失得到最优结果。在DukeMTMC-ReID和MARS数据集中做了大量的测试，实验结果表明，所提方法与Triplet算法相比能够有效提高行人再识别的平均精度（mAP）和准确率，并且对于人体姿势变化、视角变化和相似外观目标的干扰具有出色的性能表现。

Person re-identification in video sequence based on spatial-temporal regularization

Due to the interference of various factors in the complex situation of reality, the errors may occur in the person re-identification. To improve the accuracy of person re-identification, a person re-identification algorithm based on spatial-temporal regularization was proposed. Firstly, the ResNet-50 network was used to extract the features of the input video sequence frame by frame, and the series of frame-level features were input into the spatial-temporal regularization network to generate corresponding weight scores. Then the weighted average was performed on the frame-level features to obtain the sequence-level features. To avoid weight scores from being aggregated in one frame, frame-level regularization was used to limit the difference between frames. Finally, the optimal results were obtained by minimizing the losses. A large number of tests were performed on MARS and DukeMTMC-ReID datasets. The experimental results show that the mean Average Precision (mAP) and the accuracy can be effectively improved by the proposed algorithm compared with Triplet algorithm. And the proposed algorithm has excellent performance for human posture variation, viewing angle changes and interference with similar appearance targets.