Multi-attribute depth characteristic-based vehicle re-recognition method

Abstract

The invention discloses a multi-attribute depth characteristic-based vehicle re-recognition method. The method comprises the steps of extracting, by using a characteristic extraction model, a depth characteristic of a tested image set of an Ath pooling layer, obtaining, by using the depth characteristic of the tested image set and a W matrix, a Mahalanobis distance between a depth characteristic of a tested image in a searched set and a depth characteristic of a target image in a candidate set, ranking in an ascending order according to the Mahalanobis distance so as to obtain a similarity ranking result of the tested image and the target image, wherein tested image sets comprise a seeking set and a searching set, the tested image sets refer to images comprising vehicles, and the characteristic extraction model is trained through steps of accessing a vehicle multi-attribute classifier behind the Ath pooling layer of GoogLeNet, so as to obtain improved GoogLeNet, training by using training images to improve GoogLeNet so as to obtain the characteristic extraction model. The method simplifies the model training process and greatly improves re-recognition accuracy, and the model has strong generalization performance.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and more specifically relates to a vehicle re-identification method based on multi-attribute deep features. Background technique [0002] Vehicle re-identification is an important research direction in the field of computer vision, focusing on the recognition of specific target vehicles without license plate information under cameras without public view. As a newly emerging research field, although vehicle re-identification is of great significance to intelligent transportation and other aspects, there are still few related studies. One of the current mainstream methods is to use an end-to-end deep neural network to train a convolutional neural network through multiple sets of image pairs of the same target and different targets, and simultaneously pursue the minimization of the intra-class distance and the maximization of the inter-class distance during training. Another method is to find...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a vehicle re-identification method based on multi-attribute deep features, thereby solving the technical problems of the prior art such as complicated training process, poor re-identification effect, and low accuracy rate

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