Human body movement tracking method based on bidirectional Markov Monte Carlo particle filter

A Markov Monte Carlo and particle filter technology, applied in instruments, image data processing, computing, etc., can solve the problem of no identification process, and achieve the effect of good tracking results and accurate identification results

Inactive Publication Date: 2018-08-17
COMMUNICATION UNIVERSITY OF CHINA
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  • Claims
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Problems solved by technology

[0005] However, in traditional tracking methods, the identification process is not performed, or the identification process is performed after the tracking process

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  • Human body movement tracking method based on bidirectional Markov Monte Carlo particle filter
  • Human body movement tracking method based on bidirectional Markov Monte Carlo particle filter
  • Human body movement tracking method based on bidirectional Markov Monte Carlo particle filter

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Embodiment Construction

[0019] The present invention will be described in further detail below with reference to the accompanying drawings, so as to make the present invention clearer and easier to understand. Those skilled in the art would recognize that the described embodiments can be modified in various ways or combinations thereof without departing from the spirit and scope of the invention. Accordingly, the drawings and description are illustrative in nature and not intended to limit the scope of the claims. Also, in this specification, the drawings are not drawn to scale, and like reference numerals denote like parts.

[0020] Combine below Figure 1-3 Embodiments of the present invention will be described in detail.

[0021] refer to figure 1 , the human body motion tracking method of the present invention comprises the following steps:

[0022] S1 establishes a video space-time volume model, including: collecting multiple sports training data 1-N corresponding to various sports types, re...

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Abstract

The invention discloses a human body movement tracking method based on a bidirectional Markov Monte Carlo particle filter. The method comprises the steps of building video time-space body models, andextracting model characteristics of the video time-space body models; selecting a frame image in a target video time-space body, performing bidirectional particle filtering on the image through the bidirectional Markov Monte Carlo particle filter to obtain a group of solutions, matching the obtained solutions with the model characteristics of the video time-space body models, and predicting a movement type according to a matched optimal solution; and feeding back the predicted movement type to an adjacent frame image for updating a movement type of the adjacent frame image, then returning to continue filtering, and circulating the process until all images of the target video time-space body are subjected to particle filtering. The movement type and a movement state are predicted together,and tracking and identification are performed at the same time, so that a good tracking result and an accurate identification result are obtained.

Description

technical field [0001] The present invention relates to the technical field of human body motion tracking, in particular to a human body motion tracking method based on a two-way Markov Monte Carlo particle filter. Background technique [0002] Human motion tracking is currently a research hotspot in the field of artificial intelligence and computer vision. In recent years, many tracking methods, especially Particle Filter (PF), have achieved good tracking performance. But in the particle filter system, the accuracy of the posterior probability density depends on the effectiveness of the particles, and a very effective way to improve the efficiency of particle effectiveness is to conduct a global search. However, global search will bring a very large amount of calculation and increase the running time. [0003] In order to solve this problem, the more effective method currently used is to use the human motion prediction model. With the guidance of the human motion model, ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/246G06T7/277
CPCG06T7/251G06T7/277G06T2207/10016G06T2207/20081
Inventor 叶龙余安安钟微方力张勤
Owner COMMUNICATION UNIVERSITY OF CHINA
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