A method and system for pedestrian re-identification based on self-paced feedback

Abstract

The invention provides a pedestrian re-identification method and system based on automatic feedback. The method first divides the sample area and calculates the intimacy between the samples; then inputs the query sample, calculates the score and uncertainty of each sample, and obtains the query matching result; at the same time, quantitatively evaluates each sample on the ranking problem According to the degree of difficulty, samples that are difficult to sort are selected to form a sample set to be fed back, and submitted to the user to mark; to the search target expected by the user. The feedback technology based on self-paced learning and active learning proposed by the present invention makes full use of the amount of information in unlabeled images, selects the most difficult samples for the sorting task and provides them to users for labeling, while effectively reducing user labels, it can accurately and quickly Find the right audience.

Description

technical field [0001] The invention relates to the pedestrian re-identification problem in the field of video investigation, and belongs to a pedestrian re-identification method and system based on self-procedural feedback. Background technique [0002] Pedestrian re-identification in surveillance video is a technique for matching specific pedestrian objects in a multi-camera environment with non-overlapping illuminated areas. In the actual video investigation, investigators mainly use the moving pictures and trajectories of the same pedestrian object to quickly lock, check and track the suspected target. The traditional video investigation mode of manual browsing requires a lot of manpower and time, and it is easy to delay the time to solve the case. Pedestrian re-identification technology facilitates video investigators to quickly and accurately discover the moving pictures and trajectories of suspected targets, which is of great significance to the public security depar...

AI Technical Summary

Problems solved by technology

This method works well and has good robustness, but requires a large number of labeled samples and a large system overhead

When marking samples, it is difficult to find samples that are similar or dissimilar as a whole. Absolute similarity and dissimilarity also limit the marking of most samples. The samples with different degrees of similarity are treated uniformly, and the role of marking is not maximized.

[0007] Patent No. "CN 101539930 A", titled "A Relevant Feedback Image Retrieval Method", realizes the retrieval of target images through an image retrieval method based on segmental similarity measurement and multi-round joint feedback. The matching effect retrieved by the method is relatively good, but the calculation process is relatively complicated, and it is necessary to repeatedly train and learn multiple features in the image, which is not suitable for actual deployment in the pedestrian re-identification system

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