Human action recognition using short-time motion energy template images and PCANet features

A novel semi-supervised algorithm based on co-training is proposed in this paper. In the method, the motion energy history image are used as the different feature representation of human action; then the co-training based semi-supervised learning algorithm is utilized to predict the category of unlabeled training examples. And the average motion energy and history images are calculated as the recognition model for each category action. When recognition, the observed action is firstly classified through its correlation coefficients to the prior established templates respectively; then its final category is determined according to the consistency between the classification results of motion energy and motion history images. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition.

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Fights Behavior Detection Based on Space-Time Interest Points

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.577.659 ◽

2014 ◽

Vol 577 ◽

pp. 659-663

Author(s):

Jing Hu ◽

Xiang Qi ◽

Jian Feng Chen

Keyword(s):

Action Recognition ◽

Visual Analysis ◽

Detection Method ◽

Human Action Recognition ◽

Human Action ◽

Space Time ◽

Interest Points ◽

Behavior Detection ◽

Motion Energy ◽

Temporal Features

Human action recognition belongs to the senior visual analysis of computer vision, which involves image processing, artificial intelligence, pattern recognition and so on, is becoming one of the most hot research topic in recent years. In this paper, on the basis of comparative analysis and study towards current methods related to human action recognition, we propose a novel fights behavior detection method which is based on spatial-temporal interest point. Since most information of human action in video are indicated by the space-time interest points of video, we combine spatial-temporal features with motion energy image to describe information of video, and local spatial-temporal features are applied to extract fights behavior model by bags of words. Experimental results show that this method can achieve high accuracy and certain practical value.

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Human action recognition using motion energy template

Optical Engineering ◽

10.1117/1.oe.54.6.063107 ◽

2015 ◽

Vol 54 (6) ◽

pp. 063107 ◽

Cited By ~ 5

Author(s):

Yanhua Shao ◽

Yongcai Guo ◽

Chao Gao

Keyword(s):

Action Recognition ◽

Human Action Recognition ◽

Human Action ◽

Motion Energy

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Human action recognition using simple geometric features and a finite state machine

Image Processing & Communications ◽

10.2478/v10248-012-0079-y ◽

2013 ◽

Vol 18 (2-3) ◽

pp. 49-60 ◽

Cited By ~ 2

Author(s):

Damian Dudzńiski ◽

Tomasz Kryjak ◽

Zbigniew Mikrut

Keyword(s):

Action Recognition ◽

Finite State Machine ◽

Recognition Rate ◽

Human Action Recognition ◽

Human Action ◽

Video Stream ◽

State Machine ◽

Recognition Algorithm ◽

Finite State ◽

Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

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