Video pedestrian re-identification method based on multi-attention heterogeneous network

Abstract

The invention discloses a video pedestrian re-identification method based on a multi-attention heterogeneous network, and belongs to the field of image processing. The method comprises the following steps: constructing and training a multi-attention heterogeneous network; and performing feature extraction on the video of the known pedestrian ID and the video of the undetermined pedestrian ID by using the trained network, and the pedestrian ID is judged according to the cosine distance between the two features. According to the method, Soft attention and non-local attention are introduced intothe OSNet network; according to the method, the Soft attention is utilized to pay attention to pedestrian area features in an image, the learning ability of non-local attention to space-time featuresin a video sequence is utilized to improve feature representation of the video sequence, features which are more robust and more discriminative are extracted, and the recognition accuracy is improved.And meanwhile, the features of the specific frame are selected as local feature learning network branches, so that the learning of pedestrian local features is enhanced while the pedestrian global features in the video sequence are learned, and the performance of the network in video pedestrian re-identification is improved.

Description

technical field [0001] The invention belongs to the research field of pedestrian re-identification in image processing and machine vision, and more specifically relates to a video pedestrian re-identification method based on a multi-attention heterogeneous network. Background technique [0002] Pedestrian re-identification is a fundamental task in automatic video surveillance, and it is also a research hotspot in recent years. Video-based person re-ID aims to match video sequences of pedestrians from non-overlapping cameras. In order to realize video-based person re-identification, a typical method is to learn a mapping function to project video sequences into a low-dimensional feature space, and then determine the pedestrian ID by comparing the distance between samples. [0003] A large number of studies have proved that convolutional neural network as a mapping function has surpassed traditional manual features, and then aggregates image features through average pooling o...

AI Technical Summary

Problems solved by technology

Although these methods successfully capture the spatial and temporal features of video sequences, they only explore high-level feature aggregation for pedestrian feature representation, which may not be robust enough for fine-grained classification tasks such as video person re-identification.

[0004] In recent years, more and more attention models have been applied to various fields of computer vision. The attention model can focus on learning the most informative part of the input signal, and can effectively improve the network's ability to learn pedestrian features. However, the existing Algorithms often only use a single attention, ignoring the heterogeneous characteristics and complementary effects of different attention networks, so that the discriminativeness of the extracted pedestrian sequence features is relatively low, and the accuracy of video pedestrian re-identification is low

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