Cross-scene and cross-device shooting greatly increases the data of pedestrians. However， due to the different postures and partial occlusion of pedestrians， it is difficult to avoid the introduction of sample noise. During the clustering process， it is easy to generate false pseudo-labels， resulting in label noise and affecting the optimization of the model. In order to reduce the influence of noise， the camera-aware distance matrix is ??applied to combat the sample noise problem caused by camera offset， and the noise-robust dynamic symmetric contrast loss is used to reduce label noise. Specifically， the distance matrix that measures the similarity of pedestrian features is changed before clustering， and the camera-aware distance matrix is used to enhance the accuracy of the intra-class distance measurement， reducing the negative impact of different perspectives on the clustering effect. Combined with the noise label learning method， a robust loss is designed， a dynamic symmetric contrast loss function is proposed， and a joint loss training is used to continuously refine the pseudo-labels. Experiments are carried out on DukeMTMC-reID and Market-1501 datasets to verify the effectiveness of the proposed method.