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Human body behavior recognition method based on spatio-temporal distribution map generated by motion history point clouds

A point cloud generation and motion history technology, applied in the field of computer vision and image processing, can solve the problems of difficulty in extracting point cloud features, huge amount of point cloud data, lack of action time energy distribution, etc. Compatible and high-dimensional, solving complex effects with features

Inactive Publication Date: 2018-09-14
CIVIL AVIATION UNIV OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the calculation of DMM is simple, it only contains the action information of three visions, and cannot easily obtain the action information of other perspectives.
The spatial and temporal information of actions has an important impact on action recognition, but DMM only captures the spatial energy distribution of actions during motion, and lacks the temporal energy distribution of actions.
The method of mapping the depth image to point cloud data for human behavior recognition will bring great difficulties to the extraction of point cloud features due to the huge amount of point cloud data obtained.

Method used

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Embodiment Construction

[0022] The human behavior recognition method based on the spatio-temporal distribution map generated by the movement history point cloud provided by the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Such as figure 1 As shown, the human behavior recognition method based on the time-space distribution map generated by the motion history point cloud provided by the present invention includes the following steps carried out in order:

[0024] (1) Get the point cloud of each frame of the depth image by coordinate mapping the multi-frame depth image that has extracted the foreground in each human action sample, and then fill it into the MHPC until the depth images of all frames are traversed to get the point cloud of the action MHPC, to record the space and time information of the action;

[0025] The specific method is as follows: Motion history point cloud (MHPC) is to compress an action sequence...

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Abstract

The invention discloses a human body behavior recognition method based on a spatio-temporal distribution map generated by motion history point clouds. The human body behavior recognition method comprises the following steps: generating an MHPC (Motion History Point Cloud); generating an STDM (Spatio-Temporal Distribution Map); extracting a color moment feature vector; extracting an LBP feature vector; training and testing a KELM classifier, and finally fusing output results by adopting a decision layers to obtain a human body action type label. The human body behavior recognition method disclosed by the invention can acquire information of human body actions under different visual angles, so that the robustness of an action angle change is improved. The STDM for expressing a human body action is more comprehensive than a depth image, and extracted features are more distinctive; extracted color moment feature and LBP feature can effectively characterize human body action types, so thatthe problem of complexity in feature extraction by using the point clouds is solved. By use of decision layer-based fusion for classification, the shortcomings of incompatibility and high dimension offeature layer fusion can be avoided.

Description

technical field [0001] The invention belongs to the technical field of computer vision and image processing, and in particular relates to a human behavior recognition method based on a Spatio-temporal Distribution Map (STDM) generated by a Motion History Point Cloud (MHPC). Background technique [0002] Human behavior recognition has a wide range of applications in the fields of intelligent video surveillance, video content retrieval, human motion analysis, auxiliary medical care, etc. Experts and scholars at home and abroad have conducted a lot of research on this. Most of the initial behavior recognition methods are based on traditional RGB information, and methods such as key postures, silhouettes, and spatiotemporal features of the human body have been produced. However, because RGB information is easily affected by factors such as illumination, camera angle, and background changes, action recognition still faces challenges. In recent years, with the development of dept...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/20G06V10/40G06V10/467G06V10/56G06F18/2135
Inventor 张良刘婷婷
Owner CIVIL AVIATION UNIV OF CHINA
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