Vehicle re-identification method based on multi-task joint discriminant learning

Abstract

The invention discloses a vehicle re-identification method based on multi-task joint discriminant learning. According to the method, a plurality of tasks are jointly learned through a multi-branch network to obtain fine-grained discriminative features of a vehicle. A network obtains a network output feature vector through two branches of attribute learning and ID learning, meanwhile, a metric learning task and an ID learning task are used for constraining the feature vector, and more robust features are obtained through joint learning of the four tasks, wherein the ID learning uses an ArcFL loss function different from other methods, and the metric learning uses a Trihard loss function different from other methods. Through the proposal of an innovative network structure and the improvementof a loss function, the precision of vehicle re-identification and retrieval is significantly improved. The method is realized based on a large vehicle data set of a road monitoring scene, and can beeffectively applied to a vehicle search task.

Description

Technical field [0001] The invention belongs to the fields of image processing, computer vision and pattern recognition, and specifically relates to a vehicle re-recognition method of multi-task joint discrimination learning. Background technique [0002] In recent years, as an important object in urban traffic scenes, vehicles have attracted a lot of attention in the field of computer vision research. Vehicle re-identification technology is an important research content of intelligent transportation systems. In terms of intelligent management and safety maintenance, it is necessary to complete vehicle re-identification tasks in the face of automatic toll collection and search for specific vehicles. Common vehicle re-identification methods are usually based on metric learning methods and methods that combine model learning and metric learning. Metric-based learning methods such as those proposed by Zhang et al. (refer to Zhang’s method: Zhang Y, Liu D, Zha Z, et al. Improving tr...

AI Technical Summary

Problems solved by technology

However, the network structure of this method is simple, and the fine-grained features of the vehicle cannot be fully learned, and the accuracy of vehicle re-identification is not high enough.

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