Pedestrian re-identifying method based on coordination scale learning

Abstract

The invention discloses a pedestrian re-identifying method based on coordination scale learning and belongs to the technical field of monitoring video retrieval. First, according to color and texture features of images in a marked training sample set L, scale learning is carried out, and covariance matrixes Mc and Mt in corresponding Mahalanobis distance are obtained; and checking targets are selected randomly, the Mc and the Mt are used for Mahalanobis distance measuring, a corresponding sorting result is obtained, positive samples and negative samples are obtained, a new marked training sample set L is obtained, the Mc and the Mt are updated until an unmarked training sample set U is empty, a final marked sample set L* is obtained, the color and texture features are fused, an Mf is obtained, and a Mahalanobis distance function based on the Mf can be used for pedestrian re-identifying. Under a semi-supervised framework, the pedestrian re-identifying technology based on scale learning is studied, scale learning is carried out with the marked samples assisted by the unmarked samples, the requirement that practical video investigation application marked training samples are hard to obtain is met, and re-identifying performance under few marked samples can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of surveillance video retrieval, and in particular relates to a pedestrian re-identification method based on collaborative scale learning. Background technique [0002] In the actual video investigation, the investigator mainly quickly checks, tracks and locks the suspected target based on the moving pictures and trajectories of specific pedestrian objects under multiple cameras. The traditional video investigation mode based on manual browsing requires a lot of manpower and time, is inefficient, and easily delays the time to solve the case. Pedestrian re-identification is a surveillance video retrieval technology for specific pedestrian objects, that is, matching the same pedestrian object under multiple cameras with no overlapping illumination areas. It is convenient for video investigators to quickly and accurately discover the activity pictures and trajectories of suspected targets, which is of great si...

AI Technical Summary

Problems solved by technology

However, the timeliness requirements of actual video surveillance applications are very high. Once a case occurs, investigators often need to call back a large amount of video surveillance data in the shortest time to analyze and judge suspected targets.

However, the efficiency of manual labeling is very low, and it is difficult to fully obtain labeled training samples, resulting in a sharp decline in the performance of existing methods.

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