Video target tracking method based on twin network fusion multi-template features

A twin network, target tracking technology, applied in biological neural network models, character and pattern recognition, instruments, etc., can solve the problem of no template update process, the tracker can not adapt well to the change of target appearance, etc., to improve the template The effect of tracking accuracy, improving generalization ability, and improving accuracy

Active Publication Date: 2020-12-11
HENAN UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

However, the current target tracking algorithm based on the Siamese network does not have a template update process, or sim

Method used

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  • Video target tracking method based on twin network fusion multi-template features
  • Video target tracking method based on twin network fusion multi-template features
  • Video target tracking method based on twin network fusion multi-template features

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Embodiment Construction

[0041] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0042] A video target tracking method based on Siamese network fusion multi-template features, specifically comprising the following steps S1 to S6.

[0043] S1. Input picture I according to the first frame of the video sequence 1 and bounding box information B 1 Crop out the original template Z 1 , according to the subsequent frame input picture I i Crop out the target area X i ,i∈[2,n].

[0044] S2, put Z 1 and x i Send it to the offline pre-trained twin network to extract features, and get the feature φ(Z 1 ) and φ(X i ), the specific off-line pre-training methods are S2.1 to S2.5.

[0045] In step S2, the twin network has two branches, the template branch and the detection branch. The network structures of the two branches are both modified AlexNet (Alex network is a convolutional neural network structure proposed by Alex...

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Abstract

The invention relates to a video target tracking method based on twin network fusion multi-template features, and provides a semi-supervised template online updating strategy, when a to-be-tracked target in a video sequence has complex conditions such as occlusion, deformation and illumination change, the target change and the occluded condition are evaluated by calculating an APCE value and template similarity, when the appearance of the target is greatly changed, the features extracted from the previous frame of picture are fused with the original template features to obtain a new template with higher expression capability, so that the method is favorable for adapting to various complex conditions; in order to improve the generalization ability of the model and adapt to multiple types oftargets, a regularization technology is adopted in the training process to prevent model overfitting; in order to further improve the speed of the algorithm, only an original template is adopted fortracking in a non-complex situation, so that the calculated amount is greatly reduced, and the method provided by the invention achieves a higher running speed than other methods under the condition of obtaining better tracking performance.

Description

technical field [0001] The invention relates to the field of video target tracking, in particular to a video target tracking method based on twin network fusion multi-template features. Background technique [0002] Video target tracking technology is based on the bounding box information of any object to be tracked given in the first frame of the video sequence, and predicts the position and scale of the bounding box of the same target in subsequent frames. It is widely used in autonomous driving, video surveillance and man-machine areas of interaction. Traditional correlation-based filtering methods use manual features to build filter templates and update them online, such as histogram of oriented gradients (Histogram Of Oriented Gradient, HOG), Haar-like features, and local binary features (Local Binary Pattern, LBP), etc. A series of candidate frames are given first, and then all candidate frames are correlated with the filter template to obtain the confidence of each c...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/46G06N3/045G06F18/253
Inventor 孙力帆杨哲俞皓芳张金锦常家顺王旭栋陶发展司鹏举付主木
Owner HENAN UNIV OF SCI & TECH
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