A Low-Resolution Pedestrian Relearning Method Based on Semi-Coupled Dictionary Pairs of Asymmetric Mapping

Abstract

The invention belongs to the technical field of pedestrian re-identification, and discloses a low-resolution pedestrian re-learning method based on an asymmetric mapping semi-coupled dictionary pair. Aiming at this scene, a pair of asymmetric mapping and a pair of high- and low-resolution video dictionaries are learned at the same time. And a projection matrix, and use the learned mapping and dictionary to transform the features of the low-resolution video into discriminative high-resolution features, which not only reduces the variables within each video, but also compensates for the gap between high and low resolution videos. The gap makes the discrimination between different pedestrians clearer. The present invention effectively solves the problem that there may be different video resolutions in identification, especially the problem that the pedestrian needs to be re-identified in the high-resolution video set according to the low-resolution video of a pedestrian; the present invention can be effectively applied to different resolutions On the video object, and significantly improve the accuracy of pedestrian re-identification.

Description

technical field [0001] The invention belongs to the technical field of pedestrian re-identification, in particular to a low-resolution video re-learning method based on an asymmetric mapping semi-coupling dictionary. Background technique [0002] Currently, the closest prior art: [0003] In the field of person re-identification, a class of feature representation-based methods focuses on designing a robust and discriminative feature representation for matching. For example, a discriminative feature representation model is established by utilizing category information to overcome the problem of large appearance differences between different images of the same person. Another class of methods based on matching model learning focuses on how to learn a discriminative matching model, and most of these methods adopt metric learning techniques to learn the matching model. Hirzer et al. proposed a discriminative Mahalanobis metric learning method that learns a distance metric from...

AI Technical Summary

Problems solved by technology

[0010] (1) The application scenarios of existing pedestrian re-identification methods are mostly still images, which cannot recover or process the visual information in low-resolution pedestrian images. Some video-based pedestrian re-identification methods will extract pedestrians from video Spatio-temporal features, and use spatio-temporal features for matching in normal resolution mode, and the calculation of spatio-temporal features is also based on visual information, which also cannot handle super-resolution video re-identification well

[0011] (2) The existing semi-coupled dictionary learning method tends to deal with image super-resolution restoration tasks, but it is directly applied to super-resolution person re-identification tasks. Since the discriminator and regularization items are not designed, the learned dictionary pairs and mapping matrices may not be discriminative and efficient

[0012] (3) Since pedestrian videos often contain noise, the dictionary pair learned directly by semi-coupled dictionary learning technology cannot describe the essential relationship between high and low resolution images well

[0014] (1) Low-resolution images or low-resolution videos will cause loss of visual information and affect the performance of spatiotemporal features

It is necessary to restore the low-resolution video, and reduce the mapping error between the coding coefficients of the high-resolution and low-resolution image features as much as possible, otherwise it is difficult to effectively identify the super-resolution video

[0015] (2) The existing super-resolution restoration methods are designed to improve human visual perception, rather than machine perception that is beneficial to recognition, and cannot be directly applied to the problem of pedestrian re-identification, and the recognition effect will be suboptimal

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