A multi-TOF camera human body noise reduction method based on a downward inclination angle direction

Abstract

The invention discloses a multi-TOF camera human body noise reduction method based on a downward inclination angle direction. The method comprises the following steps: obtaining three-dimensional point cloud data of human bodies of a plurality of TOP cameras; mapping the three-dimensional point cloud data of all the human bodies into a two-dimensional image; performing morphological filtering processing on all the two-dimensional images, so that filtered two-dimensional images can be obtained; performing background difference processing on the filtered two-dimensional image to obtain a depth image only having a human body; carrying out endpoint identification processing on each depth image with only a human body to obtain each endpoint; and through each endpoint, performing posture recognition on the human body to obtain specific probability classification, so that noise reduction of the human body in the depth image is realized. According to the invention, the problems of high complexity and high time consumption of a three-dimensional human body posture recognition algorithm are solved; the problems that a single TOF is low in inherent detection precision, high in noise, low in pixel resolution and limited in view field range, and depth information of a larger-angle space or a larger-range space is combined together are solved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and system for reducing human body noise of multiple TOF cameras based on a downward tilt direction. Background technique [0002] In the prior art, the working principle of the TOF camera is as follows figure 1 As shown, TOF is the abbreviation of Time of Flight, literally translated as the meaning of flight time. The so-called time-of-flight 3D imaging is to continuously send light pulses to the target, then use the sensor to receive the light returned from the object, and obtain the distance information of the target object by detecting the flight (round-trip) time of the light pulse, which is the depth image (hereinafter referred to as the depth image) ). [0003] At present, human body detection through TOF cameras is also an important research direction in the field of computer vision. At present, most methods for human body posture detection are...

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Problems solved by technology

[0006] Furthermore, it is necessary to maintain a high accuracy rate of gesture recognition and reduce the complexity of the algorithm to realize that the embedded technology is not yet mature, and the algorithm complexity is high, resulting in more time-consuming. For detection scenarios that require high frame rates also does not apply;

[0007] At present, a single TOF has inherent shortcomings such as low detection accuracy, high noise, and low pixel resolution, and there are limitations in the shooting angle and field of view of a single TOF. For the combination of depth information in 360-degree space or a larger space Together there is currently no complete solution

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