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Video target tracking method based on pyramid convolution

A target tracking and pyramid technology, applied in image data processing, instrumentation, computing, etc., can solve problems such as target drift and target loss, and achieve the effects of simple framework, target loss prevention, and good performance

Pending Publication Date: 2021-01-05
BEIJING UNIV OF TECH
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Problems solved by technology

Based on the existing tracking model based on the full convolution twin network, the method extracts the multi-scale information of the tracking target by combining the pyramid convolution module, and then solves the problems of target drift and target loss caused by scale changes in the existing technology. technical problem

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  • Video target tracking method based on pyramid convolution
  • Video target tracking method based on pyramid convolution

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Embodiment Construction

[0041] In order to more clearly illustrate the technical solutions in the embodiment of the present invention or the prior art, the following will further explain in conjunction with the data sets, models, frameworks, model flow charts in the drawings and experimental results used in the experiments. In the experiment, the ILSVRC2015 data set is used as the training data set, the OTB100 data set is used as the test data set, the full convolutional twin network model based on pyramid convolution is used as the model of the method of the present invention, and the Pytorch framework is used to program to realize the present invention The method of the present invention is compared with the baseline algorithm SiamFC on the OTB100 data set for accuracy and success rate through experiments.

[0042] figure 1 It is a flow chart of a video object tracking method based on pyramidal convolution provided by an embodiment of the present invention. Such as figure 1 As shown, the method i...

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Abstract

The invention discloses a video target tracking method based on pyramid convolution, and the method comprises the following steps: cutting out target template images and search region images corresponding to all images in an image sequence training set according to the position and size of a tracking target in the images; constructing a full convolution twin network based on pyramid convolution; training the full convolution twin network based on pyramid convolution by using the constructed image sequence training set to obtain a trained full convolution twin network; and carrying out single target tracking by using the trained pyramid convolution-based full convolution twin network. In the target tracking process, pyramid convolution is added on the basis of a traditional full convolutiontwin network, convolution kernels of different scales and depths are used for extracting and fusing multi-scale features, detail information of different levels in a scene is extracted, and then robustness and accuracy of a tracking system are improved.

Description

technical field [0001] The invention belongs to the intersection field of digital image processing, deep learning and image recognition, and more specifically relates to a video target tracking method based on pyramid convolution. Background technique [0002] Visual object tracking is a research direction in the basic research topics in the field of computer vision. It is widely used in the fields of intelligent monitoring, human-computer interaction, intelligent transportation and unmanned driving. Related research work has always been a research hotspot in the field of computer vision. The task of video object tracking is to correctly identify and locate the tracking object of each frame of image in the video, and at the same time ensure the consistency of object tracking. Due to the complexity of natural scenes, the sensitivity of targets to illumination changes, the requirements for real-time and robustness of tracking, and the existence of many factors such as occlusio...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/20G06T7/70
CPCG06T7/20G06T7/70G06T2207/10016G06T2207/20081G06T2207/20084G06T2207/20132
Inventor 张斌安宁徐雪丽邓米克肖创柏
Owner BEIJING UNIV OF TECH
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