Object Tracking Method and System Based on Triple Convolutional Network and Perceptual Interference Learning

Abstract

The invention discloses a target tracking method and system based on triple convolutional network and perceptual interference learning, which belongs to the field of target tracking research in image processing and machine vision. The method includes: inputting the video to be tracked into the triple convolutional network to obtain target tracking Results; the training of the triple convolutional network includes: constructing the triple convolutional network, obtaining the positive sample pair and the negative sample pair from the data set to obtain the training set; using the training set to train the triple convolutional network, two images of each sample pair in the training set Input the template branch and the detection branch respectively, or input the first frame branch and the detection branch respectively; the template branch and the first frame branch respectively extract the appearance model feature map, and compare the two appearance model feature maps with the feature map of the detection branch respectively. Cross-correlate to get two response maps; respectively calculate the loss of the two response maps for backpropagation, and thus obtain a trained triple convolutional network. The method of the invention has higher target tracking accuracy.

Description

Technical field[0001]The present invention belongs to the target tracking research in image processing and machine vision, and more particularly to a target tracking method and system based on triple convolutional network and perceived interference learning.Background technique[0002]As an important research direction in the field of computer visual, the main task of target tracking is to accurately and reliably predict the location and size of the target in the case where the target initial position is given to the target.[0003]There are many challenging factors in the current target tracking algorithm, primarily divided into internal factors and external factors. The intrinsic factor is the change of the target itself, such as the rapid movement, rotation, deformation, etc. of the target. The external factors are mainly the changes in the external environment, such as the goal being partially or completely occluded, and the light changes in the target area are severe.[0004]Target T...

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Problems solved by technology

[0006] Aiming at the above defects or improvement needs of the prior art, the present invention provides a target tracking method and system based on triple convolutional network and perceptual interference learning, thereby solving the existing problems in the prior art that are difficult to distinguish between intra-class interference and target tracking. Track inaccurate technical issues

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