The invention provides a covered pedestrian re-identification method based on adaptive deep metric learning and relates to the computer vision technique. The method is characterized by, to begin with,designing convolutional neural network structure, which is robust to coverage, and extracting middle-and-low-level semantic features of a pedestrian image in the network; then, extracting local features robust to coverage, and combining global features, then, studying high-level semantics features, learning features sufficiently discriminating for pedestrian identity change through adaptive neighbor deep metric loss, and combined with classification loss, finishing update learning of the whole network quickly and stably; and finally, according to a trained network model, extracting output ofa first full connection layer as feature representation from a test image and finishing subsequent feature similarity comparison and sorting to obtain a final pedestrian re-identification result. Themethod effectively improves robustness of features to coverage.