# Cross-Camera Erased Feature Learning for Unsupervised Person Re-Identification
###### Abstract  
Most supervised person re-identification methods show their excellent performance, but using labeled datasets is very expensive, which limits its application in practical scenarios. To solve the scalability problem, we propose a Cross-camera Erased Feature Learning (CEFL) framework for unsupervised person re-identification that learns discriminative features from image appearances without manual annotations, where both of the cross-camera global image appearance and the local details are explored. Specifically, for the global appearance, in order to bridge the gap between images with the same identities under different cameras, we generate style-transferred images. The network is trained to classify the original images, the style-transferred images and the negative samples. To learn the partial details of the images, we generate erased images and train the network to pull the similar erased images together and push the dissimilar ones away. In addition, we joint learn the discriminative global and local information to learn a more robust model. Global and erased features are used together in feature learning which are successful conjunction of BFNet. A large number of experiments show the superiority of CEFL in unsupervised pedestrian re-identification. unsupervised; cross camera; feature discriminative learning; person re-identification +
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samples. On the other hand, there are huge gaps between images of different cameras. Training one image with multiple camera style benefits the cross-camera matching and enhances the robustness of the model. To dig into the local details of images, we generated random erased images as the positive samples. As shown in Figure 1, comparing with the global appearance, the partial images of the same identity look more similar. Since the partial images are more robust to changes in pose, lighting, and perspective, the similarity between partial features are adopted to train the model in the unsupervised manner. With the generated images and the designed auxiliary tasks, we propose a Cross-camera Erased Feature Learning (CEFL) framework for unsupervised person re-identification. The framework can be divided into three parts: cross-camera global feature learning, erased partial feature learning, and joint global and partial feature learning. Specifically, to learn the global features, the intra-class gaps are minimized while the inter-class gaps are maximized in the global feature space. To define positive samples and negative samples and learn more discriminative features across cameras, generated images of different camera styles are utilized. To learn the partial features, a Batch Feature Erase Network (BFENet) is used to randomly erase a part of the feature maps in the same batch to force the network to learn local details of pedestrian pictures. The framework is learned to pull the similar erased feature vectors together and push away the dissimilar features. To strengthen the model and get more robust feature, we also learn the global and partial appearance jointly. We take the erased features as positive samples and determine negative samples in mini-batch image set to form a triplet for pedestrian feature determination learning. The main contributions of our work are as follows:  
* We use generated images to reduce the gap between cameras. Generated images can provide additional supervision information for unsupervised person re-identification. * We use BEFNet to generate erased images so that the network can learn the features of local detail. At the same time, the similarity calculation between erased parts is more accurate and more suitable for unsupervised person re-identification. * We join to learn global and erased parts to improve the robustness of CEFL. Global and erased features are used together in feature learning which are successful conjunction of BFENet.