融合随机擦除和残差注意力网络的行人重识别

传统的行人重识别方法依赖人工构造视觉特征,容易受到其他外界因素的影响,识别精度低.深度学习模型能自主地提取特征,但随着网络层数的加深会出现梯度消失情况,残差网络能缓解梯度消失问题,但提取出的特征信息未被合理使用.行人部分图像被遮挡是影响行人重识别准确性的另一个重要因素.针对上述问题提出了融合随机擦除和残差注意力网络的行...

Pedestrian Re-identification Combining Random Erasing and Residual Attention Network

Traditional pedestrian re-recognition methods rely on artificially constructed visual features, which are easily affected by other external factors and have low recognition accuracy. The deep learning model can extract features autonomously, but as the number of network layers deepens, the gradient disappears. The residual network can alleviate the gradient disappearance problem, but the extracted feature information is not used rationally. Partial occlusion of pedestrian images is another important factor affecting the accuracy of pedestrian re-identification. To solve the above problems, this paper proposes a pedestrian re-recognition algorithm combining random erasing and residual attention network. The algorithm：first, on the basis of the residual network, the attention mechanism module is introduced, and the discriminative ability of the network is improved by strengthening the useful features and the features with little inhibition. Second, introduce random erasing data enhancement method in order to reduce the over-fitting phenomenon, at the same time improve the network generalization ability, and solve the occlusion problem in pedestrian re-identification. Third, using triplet loss to supervise and train the fusion network to achieve better clustering effect of samples in the feature space and improve the accuracy of pedestrian re-recognition. Experiments show that the algorithm can obtain higher recognition accuracy on the Market-1501 dataset and DukeMTMC-reID dataset.