Pedestrian retrieval method based on sketch image semi-coupling measurement identification dictionary learning

Abstract

The invention belongs to the technical field of traffic pedestrian image processing, and discloses a pedestrian retrieval method and system based on sketch image semi-coupling measurement identification dictionary learning. The method comprises the following steps of establishing an own heterogeneous pedestrian retrieval database, extracting features from images, and marking different persons withcorresponding colors; processing the extracted sample characteristics, introducing a dictionary learning method, and learning the dictionary pairs of the heterogeneous data; learning a mapping matrixfrom a sketch image set and a normal image set; introducing the identification algorithm learning measurement. The method has the advantages that the problem of lacking heterogeneous pedestrian datasets in the field of heterogeneous pedestrian retrieval is solved, and the semi-coupling metric identification dictionary learning (SMD2L) technology is put forward for the first time. According to the technology, a semi-coupling mapping matrix can be learned from heterogeneous samples and dictionary pairs, so that the difference between the heterogeneous data is reduced to a certain extent, and an ideal retrieval effect is achieved on a new SINPID data set.

Description

technical field [0001] The invention belongs to the technical field of traffic pedestrian image processing, in particular to a pedestrian retrieval method based on semi-coupling metric discrimination dictionary learning of sketch images. Background technique [0002] Currently, the closest prior art: [0003] Heterogeneous pedestrian retrieval between sketch images and ordinary images plays an important role in public security and criminal investigation. The purpose of heterogeneous pedestrian retrieval (HPR) is to retrieve images of the same person from heterogeneous image sets for recognition. Although pedestrian retrieval plays an important role in public safety and criminal investigation, the research is still rare. So far, in the field of pedestrian recognition, there is no pedestrian retrieval problem between sketch images and ordinary images (SINPR). data set. Therefore, it is necessary to collect a pedestrian dataset (SINPID) of sketch images and ordinary images. ...

AI Technical Summary

Problems solved by technology

The problem of pedestrian retrieval between sketch images and ordinary images that cannot be analyzed

[0008] (2) In the prior art, the semi-coupled mapping strategy is not used to connect the relationship between the sketch image and the ordinary photo to reduce the difference between heterogeneous samples

[0009] (3) No combined learning metric matrix can reveal the intrinsic projection of heterogeneous data

[0010] (4) For sketch images and ordinary photos in complex scenes, prior art dictionaries do not have good applicability to

[0011] (5) The existing technology does not combine discriminant constraints to make the same category compact and separate different categories, which is not conducive to retrieval and classification

[0020] However, directly applying existing person re-identification methods to SINPR will limit their performance

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