A spiking neural network target tracking method and system based on event camera

Abstract

The invention belongs to the field of target tracking, and in particular relates to a pulse neural network target tracking method and system based on an event camera. The method includes acquiring an asynchronous event data stream in a target high dynamic scene through an event camera; dividing the asynchronous event data stream into milliseconds event frame images with high temporal resolution; take the target image as the template image and the complete image as the search image, train a twin network based on the spiking neural network, which includes a feature extractor and a cross-correlation calculator, and the image passes through the feature extractor After the feature map is extracted, the cross-correlation calculator is used to calculate the result of the feature map; the trained network is used to interpolate and upsample the result of the feature map to obtain the position of the target in the original image to achieve target tracking. The invention reduces the transmission delay of the image data and the calculation delay of the target tracking algorithm, and improves the accuracy of the target tracking in the high dynamic scene.

Description

technical field [0001] The invention relates to the field of target tracking, in particular to a method and system for target tracking of an impulse neural network based on an event camera. Background technique [0002] Recognition and tracking of moving objects is a hot issue in the field of computer vision, and has a wide range of applications in human-computer interaction, video tracking, visual navigation, robotics, and military guidance. At present, there are two mainstream technical routes for target tracking based on correlation filtering and based on deep learning. [0003] The method based on correlation filtering is fast, but the feature extraction ability is limited, and the effect is poor when facing the problem of scale transformation and target loss. The method based on deep learning has good feature expression ability and higher tracking accuracy, but it is accompanied by an increase in the amount of calculation, which is limited in terms of real-time trackin...

AI Technical Summary

Problems solved by technology

[0003] The method based on correlation filtering is fast, but the feature extraction ability is limited, and the effect is poor in the face of scale transformation and target loss.

The method based on deep learning has good feature expression ability and higher tracking accuracy, but it is accompanied by an increase in the amount of calculation, which is limited in real-time tracking and is greatly affected by light, so it is not suitable for high-speed tracking. dynamic scene

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