A pedestrian re-identification method based on deep learning

Abstract

The invention discloses a pedestrian re-identification method based on deep learning, which includes the following steps: S1, propose a deep network structure suitable for pedestrian re-identification, and obtain their similarity scores from the bare pixels of the original image; S2, propose A learning sorting algorithm, used to guide the learning of the deep network; S3. Sampling the sorting unit for the training samples, and training the deep network using the stochastic gradient descent algorithm; S4. After the deep network training is completed, for the pedestrians under a shot, the network directly Compute the similarity score between it and the candidate image in another shot to get a matching result. The present invention establishes the mapping from the original image pair to the corresponding similarity score through the method of deep convolutional neural network. The input of the network is the pixel value of the original image, does not require any preprocessing and manual feature design, and can utilize large-scale data More discriminative and expressive features are learned, which greatly improves the effect of pedestrian re-identification.

Description

technical field [0001] The invention relates to the research field of pedestrian re-identification, in particular to a pedestrian re-identification method based on deep convolutional neural network for feature expression and similarity measure learning. Background technique [0002] At present, large-scale video surveillance networks have been popularized in various public places, such as railway stations, hospitals, airports and other places are the key areas of video surveillance. However, due to factors such as cost control and privacy rights, the monitoring network does not fully cover all areas, that is, the monitored areas are discontinuous. This brings great challenges to cross-camera video analysis (such as cross-camera pedestrian tracking, abnormal behavior detection and crowd flow analysis, etc.). To mine the high-level semantic information of pedestrians in the camera network through video surveillance technology, a key premise is to associate the same pedestrian...

AI Technical Summary

Problems solved by technology

[0003] The pedestrian re-identification problem is a very challenging problem

The second type of method uses labeled samples for supervised learning, and the effect is usually better than the first type of method, but there are still three main defects: (1) This type of method first extracts features, and then learns the distance measure for the extracted features, so , the performance of the learned distance metric is largely limited by the expressiveness and discriminativeness of hand-designed features; (2) this type of method treats the two important modules of feature extraction and distance learning in isolation, and usually only obtains a single (3) The distance metric learned by this type of method is specific to the current scene. When migrating to another new scene, the matching effect will be greatly reduced.

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