| This thesis focuses on subject-independent natural action recognition. Here, "subject-independent" means that it is unnecessary to build specific action models for each subject; "natural action" refers to daily actions with no training needed for each subject. It has broad applications in intelligent human-computer interaction, smart surveillance, content-based retrieval, model-based very low bit-rate video compression, etc. However, as human body is a complex articulated object, it is difficult to obtain the precise motion parameters. More importantly, with large scatter in the motion parameters, it is nearly impossible to get satisfying results with conventional recognition algorithms. Therefore, subject-independent natural action recognition is a very challenging research topicTo overcome the demerits of conventional algorithms, subject-independent natural action recognition algorithm with Primitive-based Dynamic Bayesian Network as its core is proposed in this thesis, which can obtain more precise motion features and reduce scatter of the motion parameters, thus improving considerably the recognition rate and robustness. The research focuses on the following four points:1. Primitive-based Human Motion RepresentationThe concept of primitives is proposed in this thesis to represent the essential features of human motion. Inferred by high-level knowledge, Primitives are distinctive features which give a best representation of the system state and describe the context information related with human actions and the motion information representing human action state as well as pose. Primitives can reduce the scatter of the motion parameters greatly and improve the subject-independent action recognition rate and robustness.As the parametric representation of primitives is low dimensional, the integration of primitives and state-based recognition algorithms helps realize some more complex recognition models. 2. DBN-based Mulit-information Fusion for RecognitionDynamic Bayesian Networks are first introduced in this thesis for natrual actionrecognition. In the recognition of dynamic system, different structures of Dynamic Bayesian Network are designed for different problems, in which many kinds of weak information are fused to be strong information. And in accordance with the source, confidence and importance, each kind of information is given a weight, which is very useful for multi-information optimization and inference.In this thesis, primitives and Dynamic Bayesian Networks are combined to be Primitive-based Dynamic Bayesian Networks, which have the both merits, thus improving greatly the recognition rate and robustness of the system.3. Integration of Background Auto Compensation and Background Mapping for Human Silhouette SegmentationTo segment precisely human silhouette, it is imperative to remove the aperture auto-adjusting effect and delete the human shadow area. For the aperture auto-adjusting, background auto compensation approach is proposed in this thesis to remove the effect by auto-compensating the background image. To remove the human shadow area, a fast segmentation approach based on stereo background mapping is given. In this thesis, the integration of the two approaches not only segments the human silhouette precisely, but also reduces the computation and improves the robustness. 4. Global Information-based Feature DetectionOn feature detection, an algorithm based on global information is presented in this thesis. Compared with the conventional algorithms which can only get the motion information of two hands, this algorithm can obtain multi-information through the global information of human silhouettes, including human body translation information, rotation information, elbow angles and the two hand trajectories. Therefore, it gives a more detailed description of the subject-independent natural actions, thus improving the recognition ability.In the experiments, the recognition results of four models are compared, namely, HMM, CHMM, PCHMM and PDBN. The resul...
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