Riemannian manifold-based pedestrian re-recognition method

Abstract

The invention relates to the technical field of mode recognition, and in particular to an attribute learning and Riemannian manifold combined pedestrian re-recognition method. According to the method,a model with stronger generalization ability is disclosed through researching representations and semantic attributes of pedestrians; and through importing pedestrian attribute labels, using a convolutional neural network model and using multi-target loss functions, the model not only needs to predict pedestrian IDs, but also needs to predict various correct pedestrian attributes. Pedestrian pictures are converted into output tensors of a convolution layer through the trained convolutional neural network, so as to respectively calculate a covariance descriptor of each pedestrian picture. Thecovariance descriptors are utilized to carry out feature fusion so as to eliminate feature redundancy, so that measurements of Riemannian manifolds where the descriptors are located are researched andthen more correct similarity calculation is realized.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a pedestrian re-identification method based on the combination of attribute learning and Riemannian manifold. Background technique [0002] In surveillance video, due to camera resolution and shooting angle, it is usually impossible to obtain very high-quality face pictures. When face recognition fails, pedestrian re-identification becomes a very important substitute technology. Pedestrian re-identification refers to the technology of automatically matching the same pedestrian object under the non-overlapping multi-camera images in the illuminated area, so as to quickly and accurately discover the moving images and trajectories of the pedestrian object under the multi-camera. [0003] The traditional pedestrian re-identification method is usually based on the underlying information such as the color and texture of the pedestrian in the image or video, and the effect ...

AI Technical Summary

Problems solved by technology

However, this disadvantage lies in the relatively high requirements for image alignment. If the two images are not aligned up and down, then the phenomenon of contrast between the head and the upper body is likely to occur, which will make the model judge wrong.

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