Method for detecting human body parts by performing parallel statistical learning based on three-dimensional depth image information

Abstract

Disclosed is a human body part detection method based on the parallel statistics learning of 3D depth image information. For problems that human body parts (head, hands, and feet) are complicated in shape changes and hard to describe and so on, a novel feature that embodies the diversity of human body parts is constructed, i.e. a universal feature, a parallel statistics learning method is applied to select effective and sufficient novel features and form a parallel cascaded classifier, thus performing real-time and highly efficient detection of human body parts.

Description

technical field [0001] The invention relates to the fields of image processing, pattern recognition, human-computer interaction, visual monitoring and the like, in particular to a method for detecting human body parts based on parallel statistical learning of three-dimensional depth image information. Background technique [0002] With the gradual improvement of computer performance and the continuous deepening of computer use in various fields, the interaction technology between human and computer has become a research hotspot in the computer field. Target recognition based on dynamic sequence images has become a research topic that has attracted much attention in the field of computer vision in recent years. It mainly conducts research on detection, recognition, tracking, and understanding and description of biological characteristics from image sequences. [0003] Target detection is the most critical step in target recognition. It is the technology to study how to let th...

AI Technical Summary

Problems solved by technology

However, most of the algorithms only use the original pixels of the image as features, most of them are very sensitive to illumination changes and noise

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