Monocular video-based human skeleton tracking method

A human skeleton and skeleton technology, applied in the field of computer vision, can solve the problems of relying on image depth information and difficult to meet real-time performance, and achieve low-cost effects

Inactive Publication Date: 2016-07-06
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved in the present invention is to propose a skeleton tracking method based on monocular video in view of the existing skeleton tracking technology that relies heavily on image depth information and is difficult to meet real-time performance.

Method used

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  • Monocular video-based human skeleton tracking method
  • Monocular video-based human skeleton tracking method
  • Monocular video-based human skeleton tracking method

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Experimental program
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Embodiment Construction

[0021] refer to figure 1 , represents the flow chart of the training process using the constructed attitude database using the regression method, and the steps shown in the figure are:

[0022] a. Define the feature descriptor: use the distance-based method to quantify the difference between the silhouette and the initial skeleton, and use the two-dimensional projection point of the initial skeleton joint point as the origin to emit rays until it intersects with the silhouette outline or exceeds the range of the image. Take the ray's length as a feature value. In addition, when the 2D projected point is inside the silhouette, its eigenvalues ​​are positive, otherwise they are negative. The specific calculation formula is:

[0023] f ( o r i g i n ) = d i ...

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Abstract

The invention discloses a monocular video-based human skeleton tracking method. The method comprises the following steps: in a training stage, a, feature descriptor definition: quantifying the difference between a sketch and an initial skeleton by utilizing a distance-based method, b, feature extraction and classification: extracting features having max-relativity with a regression target through a relativity-based random fern method and classifying the features to obtain a regressand which decides the skeleton adjustment range of the current stage, and c, skeleton adjustment: adjusting the skeleton for proper times according to an addition model and outputting a cascading regressand; and in a test state: a, inputting a sketch and an initial skeleton of the first frame of a monocular video, b, gradually adjusting the initial skeleton to a final skeleton according to the cascading regressand obtained through training, and c, searching 5 skeletons mostly similar to the current frame of predicted skeleton, respectively regressing the 5 skeletons, and taking the regressed average skeleton as the initial skeleton of the next frame to predict the skeleton of the next frame. According to the method, the tracking of skeletons can be effectively realized, and the error accumulation phenomenon in the tracking process can be avoided.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a method for tracking human skeletons based on monocular video. Background technique [0002] 3D human skeleton tracking is a research hotspot in the field of computer vision, and it is widely used in video surveillance, human-computer interaction, behavior understanding, intelligent animation synthesis, virtual reality and other fields, and has broad application prospects. Its research involves computer vision, computer graphics, machine learning, image processing and artificial intelligence, and is an interdisciplinary and challenging research topic. [0003] Skeleton tracking is defined as "estimating human poses from frametoframe, tracking is used to ensure temporal coherence between poses over time". Skeleton reconstruction is defined as "estimating human poses from images". Skeleton reconstruction is the basis of skeleton tracking. In some learning-based methods...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/20G06T7/246
CPCG06T2207/10021G06T2207/30196
Inventor 童若锋聂迎陈可立李承扬
Owner ZHEJIANG UNIV
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