A pedestrian re-identification method based on dual-angle ranking optimization

Abstract

The invention discloses a double-angle sequencing optimization based pedestrian re-identification method, and belongs to the technical field of surveillance video retrieval. The double-angle sequencing optimization based pedestrian re-identification method comprises the steps of: firstly obtaining initial sequencing results through ways based on global characteristics and local characteristics; then taking an intersection set GKc(p) of front k results of the two sequencing results as initial values for inquiring a more reliable nearest neighbor; then regarding each goal gKc(j) in GKc(p) as a new inquiring factor for crossed opposite inquiry, weighting and fusing a sequence list of the opposite inquiry to obtain a whole optimized sequence list; finally, establishing a double-layer map for each gKc(j) in the GKc(p), and calculating neighbor similarity of the map to perform further rearrangement on the GKc(p) so as to further obtain a better re-identification effect.

Description

technical field [0001] The invention belongs to the technical field of surveillance video retrieval, and in particular relates to a double-angle sorting optimization pedestrian re-identification method. Background technique [0002] In actual video surveillance, investigators need to quickly check, track and lock suspect targets based on the moving images and trajectories of designated 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 cross-camera surveillance video automatic 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 grea...

AI Technical Summary

Problems solved by technology

[0006] Both global features and local features have achieved good re-identification results, but we found that their initial ranking lists are very different

Since the retrieved pedestrians based on the global feature description method may only look slightly different on the whole, but the local details are very different, and the method based on the local feature description is generally effective when the local area of ​​​​the pedestrian is occluded.

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