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3D Hunam Motion Analysis and Action Recognition

Author: CaiMeiLing
Tutor: ZouBeiZuo
School: Central South University
Course: Computer Science and Technology
Keywords: 3D human motion keyframe multi-objective optimization Probabilistic Principle Component Analysis Gaussian Model motionclassification rejection determination self-organizing incrementalmotion map
CLC: TP391.41
Type: PhD thesis
Year: 2013
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Abstract


As motion capture technology matures, obtaining massive3D motion dataset with high efficiency and effectiveness has been possible. Motion data have been widely applied to computer animation, movie production, digital entertainment, PE simulation and medical therapy as it could maintain the motion details accurately and record the real motion trace precisely. Therefore, human motion analysis based on captured data has become a popular issue. In addition, the keyframe extraction from3D human motion data, automatic recognition and classification are the most significant parts in human motion analysis and important bases and foundations for efficient management and reuse of the captured motion data.Based on the raw3D motion capture data, keyframe extraction is for the purpose of extracting the key postures which are considered as the abstract representation of the raw motion sequence. As one of the most important methods and strategies in data compression, data reduction, feature extraction and data representation, keyframe extraction technology has been universally applied to animation creation, human motion analysis and reuse, etc. Human motion recognition is aim to analyze and understand all kinds of human motions and behaviors by extracting and analyzing the parameters related to human motion features. It is universally admitted that motion recognition technology has a promising future as well as huge economical value and social value in advanced human-machine interface, recovery project, motion-sensing controller, and content-based image retrieval. This dissertation mainly focuses on three aspects:the keyframe extraction from motion data, postures recognition and motion segmentation, continuing postures recognition with rejection ability. Following is the details:(1) Research on the keyframe extraction from motion data. Two keyframe extraction methods are proposed in this dissertation by optimizing the reconstruction ability and compression rate since they are two significant factors during the keyframe extraction. In the first method, the keyframe extraction experiences two phrases:pre-selection phase and refinement phase. During the first phase, the’extreme postures’are extracted from the motion sequence as the candidate keyframes. In the second phase, the importance of the keyframe is measured by decimated error, and then we optimize the reconstruction error as well as the compression rate. As a consequence, the keyframes satisfying the demands of reconstruction ability or compression rate are extracted. The advantage of this method is that reconstruction error and compression rate can be set directly in a simple way. Concerning the competitiveness and contradictory between the reconstruction error and compression, the keyframe extraction is modeled as a multi-objective optimization problem with constraints in the second method. Consequently, a keyframe extraction method based on Pareto multi-objectives Genetic Algorithm is presented, in which a set of candidate keyframes with Pareto optimality can be obtained without any given parameters related to the threshold, which is the major advantage of this method. The experiment results demonstrate the efficiency of it.(2) Research on posture recognition and segmentation based on Probabilistic Principle Component Analysis (PPCA). Motion dataset in the same class could be represented by a uniform distribution model for the same inner dimension and similar structure of the human motion data. For each motion type, Probabilistic Principle Component Analysis (PPCA) is adopted to build its Gaussian Distribution Model, whose parameters are learnt by Expectation-Maximization (EM). With these learned models, the decision rule can be found and a polychotomizer based on the minimum error Bayes decision theory to recognize single actions is easily obtained with discriminant functions determined by these models. Then an algorithm is presented to recognize the input motion based on the polychotomizer. By extending this algorithm, an online recognition and automatic segmentation algorithm for long motion sequence including different actions is proposed since PPCA has the ability of modeling for changing information from one action to the next. The experiment results provide strong evidence for the validity of the proposed method.(3) Research on motion recognition and rejection method based on self-organizing incremental motion map. In terms of the long motion sequence involving motion types which do not appeared in training dataset, we present a motion recognition system with the ability of rejection recognition which combines Support Vector Machine (SVM) and self-organizing incremental motion map. We apply SVM to online recognition for motion data, and give the reasons why the margin information of SVM could not be applied to rejection recognition directly and simply. Meanwhile, we analyze how self-organizing map (SOM) describe the distribution of the samples. In order to improve the adaptive ability of the traditional learning method base on SOM, a novel self-organizing incremental motion map learning algorithm is put forward, in which a map with adaptive structure and size is automatically adapted according to the complexity of different motion type. And then the rejection recognition rule is acquired according to the learned motion map and used for rejection. In the last step, the key patterns learned from the same motion map by Genetic Algorithm are used for the final confirmation if the result segments are really what their types claim. Combining the advantages both of SVM and motion map, the proposed method not only can identify motion types in the training dataset, but also can reject the motion types which are out of the training dataset. The experiment results provide strong evidence for the validity of the proposed method.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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