Deep learning target tracking method based on feature map segmentation and adaptive fusion

Abstract

A deep learning target tracking method based on feature map segmentation and adaptive fusion comprises the following steps: (1) preprocessing a video in a training set to generate a training sample pair composed of a template image and a search area image; generating a response graph label; (2) constructing a deep learning network model based on feature map segmentation and adaptive fusion; (3) training the deep learning network model, and performing disk storage on the structure of the model and trained model parameters to obtain a target tracking model; (4) processing the video to be trackedto obtain a template image corresponding to the first frame and search area images corresponding to three scales corresponding to each subsequent frame to be tracked; and (5) loading the target tracking model to form three pairs of samples composed of template images and search area images, and inputting the three pairs of samples into the target tracking model to obtain the target position of each subsequent frame of the to-be-tracked video.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a deep learning target tracking method based on feature map segmentation and adaptive fusion. Background technique [0002] Target tracking means that for a given video and the tracking target in its first frame, the position of the target is continuously located in subsequent frames. Target tracking has a wide range of applications in many fields such as security video surveillance, unmanned aerial vehicle reconnaissance, military target tracking, military strikes, patient supervision, and intelligent transportation. Traditional target tracking methods include target tracking methods based on mean shift, target tracking methods based on particle filter, target tracking methods based on sparse coding and target tracking methods based on correlation filtering. With the continuous development of artificial intelligence technology and the success of deep learning ...

AI Technical Summary

Problems solved by technology

Compared with traditional tracking methods, deep learning-based methods have greater advantages in tracking accuracy. However, when the target is occluded or deformed, it is easy to cause the tracking target to be lost, resulting in a decrease in overall tracking performance.

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