Pedestrian re-identification method and system based on unsupervised learning

Abstract

The embodiment of the invention provides a pedestrian re-identification method and system based on unsupervised learning. The method comprises the following steps: firstly, obtaining two to-be-identified video frames containing a plurality of pedestrians; and inputting the two to-be-identified video frames into the pedestrian re-identification model, and determining whether the two to-be-identified video frames contain the same pedestrian or not by the pedestrian re-identification model. A pedestrian re-identification model adopted in the embodiment of the invention is constructed based on a deep convolutional neural network, and when the pedestrian re-identification model is trained, a pedestrian cyclic distribution matrix between two sample video frames containing a plurality of pedestrians is determined, and an optimization loss function is determined based on the cyclic distribution matrix. In the whole training process, no extra algorithm module or indirect supervision signal is needed, such as a pedestrian tracking module or a clustering algorithm, pedestrian features can be directly learned from the unlabeled sample video frame, pedestrian re-identification is realized, the whole training process of the pedestrian re-identification model is simplified, and the accuracy of pedestrian re-identification is higher.

Description

technical field [0001] The present invention relates to the field of computer technology, and more specifically, to a pedestrian re-identification method and system based on unsupervised learning. Background technique [0002] At present, pedestrian re-identification is an important application in the field of computer vision, which aims to identify the identity of pedestrians through the appearance information of pedestrians captured by cameras, and can perform identity verification and retrieval. Pedestrian re-identification has very important application prospects in the fields of security monitoring, smart city and smart retail. [0003] The mainstream pedestrian re-identification methods are based on deep learning and large-scale labeled data training deep convolutional neural network model as a pedestrian feature extraction model, and then judge whether they are the same identity by comparing the feature similarity between different pedestrians . Such a method has ac...

AI Technical Summary

Problems solved by technology

Another example is to unsupervisedly learn the pedestrian feature extraction model through the clustering-training iteration method. It is necessary to assign a pseudo-label to each pedestrian, but it is very likely that the distribution of the pseudo-label is unreasonable or even wrong, so it will be very large. The unsupervised learning of the pedestrian feature extraction model, resulting in the pedestrian re-identification effect cannot be guaranteed

Images