Single vision target tracking algorithm and system based on deep neural network

Abstract

The invention relates to the field of visual target tracking, and discloses a single visual target tracking algorithm and system based on a deep neural network. The basic principle of the method is asfollows: a tracking target is specified by a first frame in an image sequence, target features and to-be-searched region features are extracted by adopting the same convolutional network in subsequent frames, convolution and foreground-background distinguishing networks are performed to obtain a target position, and the width and height of a target frame are obtained through regression, so that aregion frame where the target is located is obtained; and when the confidence value of the tracking target is lower than a certain degree, considering that problems such as target loss may occur, andperforming re-search by adopting a re-search strategy to ensure the tracking effect of the target. For targets of different sizes, the sizes of the targets are input into a size adjusting module, sothat the sample cutting size is dynamically adjusted according to the target sizes. Therefore, the template can adapt to targets with different sizes and different motion characteristics, and the tracking performance is improved.

Description

technical field [0001] The invention relates to the field of visual target tracking, in particular to a single visual target tracking algorithm and system based on a deep neural network. Background technique [0002] Object tracking is an important research content in the field of computer vision, and it is widely used in military and industrial fields. The main goal of target tracking is to accurately and real-time acquire the characteristics of the specified target and follow the specified target in a video or image sequence. Target tracking technology is widely used in the military field, intelligent monitoring and other fields, and plays an important role in investigation and monitoring. [0003] At present, the more common target tracking methods include correlation filtering that appeared in the early years of the school and deep learning that appeared in recent years. Among them, the deep learning algorithm uses a deep neural network to extract and match target feat...

AI Technical Summary

Problems solved by technology

[0005] However, for different image sequences, the algorithm always crops sample frames with a fixed size. For different scenes and targets of different sizes, its adaptability is poor; the relatively fixed area to be searched makes the target easy to lose when moving fast. It is easier to make false tracking when similar targets appear in the scene

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