Vehicle re-identification method and system based on double attention mechanisms

Abstract

The invention discloses a vehicle re-identification method and system based on a double attention mechanism. The method comprises the following steps: constructing a convolutional neural network for vehicle feature extraction; constructing channel attention parts for paying attention to different channels; constructing a granularity attention component used for paying attention to the feature suitable granularity; randomly selecting multiple types of vehicles in each training batch, and randomly selecting multiple images in each type of vehicles to construct batch images; carrying out real-time data enhancement on the batch images and then inputting the batch images into a convolutional neural network; constructing a cross entropy loss function and a triple loss function after smooth regularization of the batch labels, and adding the cross entropy loss function and the triple loss function to obtain an integral loss function; and carrying out feature extraction on the trained convolutional neural network, calculating Euclidean distances between the features, and reordering the distances to obtain a vehicle re-identification result. According to the invention, the fine-grained features of the vehicle image can be better obtained, and the precision and stability of the model are improved.

Description

technical field [0001] The invention relates to the technical field of vehicle re-identification, in particular to a vehicle re-identification method and system based on a dual attention mechanism. Background technique [0002] Vehicle re-identification is a vehicle search method, which refers to inputting a query vehicle image and searching for a vehicle with the same identity as the query vehicle in the database. Vehicle re-identification is generally used in large-scale urban video surveillance networks, which can help traffic management departments quickly, accurately and conveniently discover, locate, and track target vehicles in massive traffic monitoring data. However, the unconstrained urban traffic scene has brought many difficulties to the vehicle re-identification technology, such as the variability of ambient lighting, the arbitrariness of the shooting angle, the complexity of the shooting background, the occlusion of foreground objects, etc., using the license p...

AI Technical Summary

Problems solved by technology

[0004] The above method researches and extracts local features from different directions, enhances the distinguishing ability of features to a certain extent, and improves the performance of vehicle re-identification, but in When extracting local features, a sub-network is used alone, which increases computing resources, and is prone to over-fitting problems for specific data, reducing practicability

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