联合损失优化孪生网络的行人重识别

针对行人重识别应用中行人图像易受到光照、相似着装、拍摄角度影响而出现难分样本对，导致错误匹配的问题，提出一种联合损失结合孪生网络的行人重识别优化算法。首先利用残差卷积神经网络提取图像特征，并以焦点损失（Focal Loss）和交叉熵损失的联合损失对提取的特征进行监督训练，增加模型对难分样本对的关注度；然后采用余弦距离计算图像间的相似度实现行人的重识别；最后加入重排序算法降低误匹配率。采用Market-1501和DukeMTMC-reID数据集进行实验，结果表明，该算法的匹配率分别为91.2%和84.4%，平均精度均值（mAP）分别为85.8%和78.6%。

Person re-identification based on joint loss and siamese network

In person re-identification applications, pedestrian images are susceptible to illumination changing, similar wearing and different shooting angles, so it is difficult to distinguish sample pairs, which leads to incorrect matching. Aiming at this problem, a person re-identification optimization algorithm based on joint loss and siamese network is proposed. Firstly, the residual convolutional neural network is used to extract image features. The joint loss including focal loss and cross entropy loss is used to carry out supervised traning on the extracted features, in order to increase the models attention to the indistinguishable pairs. Then, the cosine distance is used to calculate the similarity between images to realize the pedestrian recognition. Finally, a re-ranking algorithm is adopted to reduce the mismatch rate. Experimental results on open datasets including Market-1501 and Duke MTMC-reID show that the matching rate (Rank-N) is 91.2% and 84.4%, respectively, and the mean Average Precision（mAP）is 85.8% and 78.6%.