Human motion detection method suitable for depth image

Abstract

The invention provides a human motion detection method suitable for a depth image. The method comprises the following steps: firstly, dividing the image into an upper layer and a lower layer, wherein the upper layer and the lower layer are used for creating a background model by different neighborhoods, and a reference model is added while the background model is created; secondly, adjusting the parameter of the difference threshold value of an image lower-layer algorithm and comparing pixels with the background model for pixel classification in a following video; thirdly, updating the background model by different updating modes based on the classified pixels; finally, denoising false detection points. According to the method, the identification and detection rates of a human body are remarkably increased.

Description

technical field [0001] The invention belongs to the field of computer vision, and relates to a method for human body motion detection aiming at depth images. Background technique [0002] In the research of human motion vision analysis, human motion detection is a key preprocessing step, which directly affects the effect of follow-up tracking and recognition, so the human motion detection algorithm has always been a research hotspot in this field. [0003] The 3D sensor represented by Microsoft Kinect can obtain the depth image showing the three-dimensional information of the object, which provides a new way for human motion detection and analysis. Compared with ordinary color images, depth images have some obvious advantages. For example, the shadow and lighting problems that plague color images have little effect on depth images. [0004] The ViBe (visual background extractor) algorithm is also called the visual background extraction operator algorithm, which is one of th...

AI Technical Summary

Problems solved by technology

However, due to the different characteristics of depth images and ordinary color images, the method has the following problems when applied to depth images: 1) It is difficult to detect moving objects near the ground, and the specific phenomenon is that the feet connected to the ground in the image are missing

3) Still pixels are misjudged as foreground due to inherent defects of the Kinect sensor

Mainly due to the deviation of sensor accuracy: distant pixels will have certain changes or even be lost in adjacent video sequences, and the edges of objects are unstable

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