Human behavior identification method based on space-time distribution map

A technology of spatio-temporal distribution and recognition method, which is applied in the field of computer vision and image processing, can solve the problems of high computational complexity and cumbersome algorithms, and achieve the effects of high classification accuracy, complex features and increased robustness

Inactive Publication Date: 2018-09-21
CIVIL AVIATION UNIV OF CHINA
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Problems solved by technology

Although this method extracts the features of the point cloud, the algorithm is too cumbersome and the calculation complexity is high.

Method used

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  • Human behavior identification method based on space-time distribution map
  • Human behavior identification method based on space-time distribution map
  • Human behavior identification method based on space-time distribution map

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Embodiment Construction

[0021] The human behavior recognition method based on the spatio-temporal distribution diagram provided by the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] like figure 1 As shown, the human behavior recognition method based on the spatiotemporal distribution diagram provided by the present invention comprises the following steps carried out in order:

[0023] (1) The point cloud of each frame of depth image is obtained by coordinate mapping the multi-frame depth image that has extracted the foreground in each human action sample, and then fills it into the motion history point cloud (MHPC) until all frames are traversed. The MHPC of the action is obtained from the depth image to record the space and time information of the action;

[0024] The specific method is as follows:

[0025] The human action samples are selected from the MSRAction3D database. The depth images in the MSR Action3D da...

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Abstract

The invention provides a human behavior identification method based on a space-time distribution map generated based on motion history point cloud. The method comprises the following steps: generatingthe MHPC; generating the STDM; extracting HOG feature vector; and training and testing a KELM classifier, and finally, the output of the KELM classifier being a human body motion classification result and the like. The method can obtain information of body motion from different angles of view, thereby enhancing robustness of motion angle change; body motion information is more complete when represented by the space-time distribution map than represented by a depth motion map, and the extracted features are also more distinctive; the extracted HOG features can effectively represent human action categories, and thus the problem of complex feature extraction based on the point cloud is solved; and by adopting an extreme learning machine based on a Gaussian kernel function, the method has theadvantages of high classification precision and fast learning speed.

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). 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 depth sensors, depth image acquisition technology has...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06T7/55
CPCG06T7/55G06T2207/30196G06T2207/10028G06T2207/10024G06T2207/20084G06T2207/20081G06V40/20G06F18/2413
Inventor 张良刘婷婷李玉鹏
Owner CIVIL AVIATION UNIV OF CHINA
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