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Target tracking method based on compressed-sensing theory and gcForest

A target tracking and compressed sensing technology, applied in character and pattern recognition, instruments, computer components, etc., can solve the problems of large amount of computation, inapplicability, and inability to meet real-time performance, and achieve reduced computation, high precision and robustness sticky effect

Active Publication Date: 2018-04-17
BEIJING UNIV OF TECH
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Problems solved by technology

[0005] The shortcomings of the existing methods: on the one hand, the classic tracking algorithm is very good for the tracking of specific targets, and can achieve real-time performance, but it is not generalizable and cannot be applied to various occasions. , If the lighting problem is not solved well, it will easily lead to tracking failure; on the other hand, the tracking algorithm based on deep learning has greatly improved the tracking accuracy, and can basically reach an accuracy of more than 95%, but the algorithm structure is complex and the amount of calculation is high. Large, unable to meet real-time requirements

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  • Target tracking method based on compressed-sensing theory and gcForest
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  • Target tracking method based on compressed-sensing theory and gcForest

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[0026] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0027] like figure 1 As shown, the target tracking method based on compressed sensing theory and gcForest according to the present invention comprises the following steps:

[0028] S1: Select a large number of image sequences related to the target to be tracked to pre-train a gcForest network offline to extract target features;

[0029] The gcForest network first performs offline training, and the specific training process is as follows:

[0030] A typical gcForest network mainly consists of multi-particle scanning layers and cascaded ...

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Abstract

The invention discloses a target tracking method based on compressed-sensing theory and gcForest. The method includes the following steps: acquiring an initial-frame video image of a tracked target; extracting positive and negative sample image slices, and carrying out multi-scale transformation to obtain multi-dimensional vectors; extracting deep-level features of the tracked target through a gcForest network to obtain deep-level expression of the target; adopting the compressed-sensing theory to carry out dimension reduction on the features thereof to obtain final feature expression, and training a classifier; and sampling n windows around a target location of a last frame on a next-frame image, using the classifier, which is trained by the previous frame, for classification, determiningthat a window, which obtains a largest classification score, is the tracked target, and updating classifier parameters thereby. According to the method, precision of video target tracking is effectively improved, the target can be stably tracked under complex conditions, and at the same time, real-time performance of target tracking is effectively improved due to decreasing of a calculation amount.

Description

technical field [0001] The invention relates to a video target tracking method, which integrates advanced technologies in many fields such as image processing, pattern recognition, artificial intelligence, automatic control and computer, and particularly relates to a target tracking algorithm based on compressed sensing theory and gcForest. Background technique [0002] Video object tracking technology is an important issue in the field of computer vision research, and it is widely used in video surveillance, video retrieval, transportation, automatic driving, etc. Video target tracking mainly solves the problem of selecting one or more specific targets in a video image sequence, and continuously finds the position of this target in each frame in this continuous video sequence, and provides a complete target area at the same time, so as to complete Tracking tasks. This technology involves many fields, including image processing, pattern recognition, probability statistics, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/46G06F18/2411G06F18/214
Inventor 刘芳杨安喆王洪娟黄光伟路丽霞王鑫
Owner BEIJING UNIV OF TECH
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