Recognition of human body posture from a cloud of 3D data points using wavelet transform coefficients

Werghi, Naoufel; Xiao, Yijun

doi:10.1109/afgr.2002.1004135

Cited by 16 publications

(11 citation statements)

References 16 publications

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“…Many systems use complex 3-D models, and require special and expensive equipment, such as 3-D trinocular systems [7], 3-D laser scanners [8], thermal cameras [9], or multiple video cameras to extract 3-D voxels [10]. Due to the need for realtime performance and low-cost systems, we discarded complex and/or 3-D expensive solutions.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Posture Classification for Human-Behavior Analysis

Cucchiara

Grana

Prati

et al. 2005

IEEE Trans. Syst., Man, Cybern. A

157

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Abstract-Computer vision and ubiquitous multimedia access nowadays make feasible the development of a mostly automated system for human-behavior analysis. In this context, our proposal is to analyze human behaviors by classifying the posture of the monitored person and, consequently, detecting corresponding events and alarm situations, like a fall. To this aim, our approach can be divided in two phases: for each frame, the projection histograms (Haritaoglu et al., 1998) of each person are computed and compared with the probabilistic projection maps stored for each posture during the training phase; then, the obtained posture is further validated exploiting the information extracted by a tracking module in order to take into account the reliability of the classification of the first phase. Moreover, the tracking algorithm is used to handle occlusions, making the system particularly robust even in indoors environments. Extensive experimental results demonstrate a promising average accuracy of more than 95% in correctly classifying human postures, even in the case of challenging conditions.

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Section: Related Workmentioning

confidence: 99%

Probabilistic Posture Classification for Human-Behavior Analysis

Cucchiara

Grana

Prati

et al. 2005

IEEE Trans. Syst., Man, Cybern. A

157

View full text Add to dashboard Cite

show abstract

“…For example, Delamarre et al [7] proposed a method to build a 3-D human body via three or more cameras and then calculated the projection of silhouette to compare with the 2-D projection in the database. Moreover, 3-D laser scanners [8] or thermal cameras [9] were also adopted to build the 3-D human body model. However, the above 3-D solutions suffer from enormous computing time requirement and high device cost.…”

Section: Introductionmentioning

confidence: 99%

3-D Human Posture Recognition System Using 2-D Shape Features

Lin

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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This paper presents an integrated framework for recognizing 3D human posture from 2D images. A flexible combinational algorithm motivated by the novel view expressed by Cyr and Kimia [1] is proposed to generate the aspects of 3D human postures as the posture prototype using features extracted from the collected 2D images sampled at random intervals from the viewing sphere. Frequency and phase information of the posture are calculated from the Fourier descriptors (FDs) of the sampled points on the posture contour as the main and assistant features to extract the characteristic views as the aspects. Moreover, a modified particle filter is applied to improve the robustness of human posture recognition for continuous monitoring. Experimental trials on synthetic and real sequences have shown the effectiveness of the proposed method.

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“…Werghi et al [13], use wavelet decomposition for feature extraction and a Bayesian approach as pattern classifier. Human gait recognition based on motion analysis and support vector machines (SVMs) is proposed in [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Human Body Postures Based on the Truncated SVD

Zerrouki¹,

Houacine²

2014

JACN

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Abstract-In this experimental study, we propose the use of Singular Value Decomposition (SVD) coefficients as features to automatically classify human body postures. The classification process uses images extracted from a fixed camera video. A background subtraction technique is applied for human body segmentation. A truncated SVD is performed by selecting significant magnitude coefficients. And the height-width ratio of the human body is also included in the set of features. The classification is then performed using an Artificial Neural Network (ANN). Four body postures are considered in our experiments, namely: standing, bending, sitting, and lying. Evaluation results show that the proposed method achieved 90.46% classification accuracy. Truncated SVD coefficients and height-width ratio as body posture features are thus appropriate descriptors to achieve high classification accuracy. Also, the proposed method yields the best classification accuracy compared to well-known classification methods. Index Terms-Human body postures, classification, SVD coefficients, neural network. I. INTRODUCTIONHuman behavior understanding remains an interesting study area in intelligent video monitoring because it is closely related to computer vision applications and automatic surveillance. It is a very challenging and complex task due to some difficulties such as variations in people's appearance, skin color, illumination conditions, and the amount of data generated. To reduce the complexity, many previous works have focused on some specific topics such as face recognition [1], body tracking [2], and hand gesture recognition [3] from a single image or multiple images.Behavior understanding includes mainly motion pattern identification or body posture analysis and classification. Many approaches have been proposed for human posture classification. Poppe et al. [4] use a degree of freedom to estimate the body posture for a presenter in meeting environments. Hooi et al. propose a computer vision-based approach to automatically detect human body parts and estimate the human body postures from a monocular video sequence [5].However, most of the stated methods use skin color to locate hands and head. The person is also supposed to face the camera which can be considered as a limitation.Capo et al. present a computer vision algorithm to automatically model the human body skeleton using Bayesian networks classifier [6]. In [7] the body is decomposed into its Manuscript received August 25, 2013; revised October 29, 2013. The authors are with LCPTS laboratory, University of Sciences and Technology Houari Boumé dienne, Algeria (e-mail: nzerrouki@usthb.dz, ahouacine@usthb.dz). main parts, it constructs a silhouette based body model to determine the location of the body parts [8]. But recognition of human parts is not an easy task as it requires processing based on only visible parts of the body. Lutz Goldmann et al.[9] use appearance-based features for posture description such as color, shape, texture, motion, and size. Fast Fourier Tran...

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Recognition of human body posture from a cloud of 3D data points using wavelet transform coefficients

Abstract: Abstract

Cited by 16 publications

References 16 publications

Probabilistic Posture Classification for Human-Behavior Analysis

Probabilistic Posture Classification for Human-Behavior Analysis

3-D Human Posture Recognition System Using 2-D Shape Features

Automatic Classification of Human Body Postures Based on the Truncated SVD

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