Research On Human Motion Modeling And Pose Estimation From Monocular Videos

Automatically analyzing and understanding human motion and pose estimation has been an important field of computer vision for many years. This thesis focuses on five aspects for video-based human motion analyzing:3D human motion capture technology, pedestrian detection, human motion feature extraction, human motion tracking and 3D pose estimation technology. Through these methods, the human motion model is built, and to estimate human pose from monocular imagesIn order to improve accuracy of the human detection under occlusion, this paper proposes the conception of the Window edge of the Gradient of Potential Energy (WGPE) and a fast human detection method based on gradient potential energy. By using sparse-dense gradient potential windows set, the detection time of the multi-scale detection can be shortening. Cascading SVM training using weighted positive and negative samples, the occlusion sample of the human body is weighted to detect the human body under occlusion. Filter positive in the detection window, the algorithm does not require too much computational overhead increases when the detection window is filtered. In the smooth background image, the proposed method compared to the Multi-level HOG detection and Histograms of Oriented Gradients and Local Binary Pattern (HOG-LBP) methods accuracy at the same rate, spends less detection time. Experiments show that the human detection accuracy and efficiency has increased, the case for the human body in partial occlusion detection, the accuracy rate is improved markedly.To human pose estimation, the Bayesian model based on the edge contour is used to estimate human motion. We proposed a novel Bayesian method, and introduce trajectories of bones in order to improve the accuracy of the analysis.For the video analysis in the human pose estimation, another method based on Conditional Random Field (CRF) model is proposed. The human body silhouette image SIFT feature is extracted, and using SIFT feature database to estimate the pose of the human motion, and using CRF to estimate human posture.To improve pose estimation accuracy and meet the requirements of real-time, the human motion rhythmic data is automatic extracted by the proposed method EM-GM algorithm. We build dynamic color-edge features to model the human body, in which the edge information matching using Fast Directional Chamfer Matching (FDCM) method. The rhythm-based 3D motion information is used to estimate the human pose. By rhythmic movement data, the 3D human posture is estimated. Using GPLVM method to reduce the human motion data dimension and then using a local modeling of the dynamic, the 3D human body pose can be estimated.For the video image in the human body pose estimation, this thesis presents a constraint graph based video body posture estimation method, first to establish levels for human movement model, defines the human body model. And put forward relevant actions based on the movement of the cluster model, in order to reduce the search space, spanning tree algorithm proposed RPC node graph, and refinement of the merger of RPC nodes, node splitting and spanning tree balancing algorithm. According to RPC node graph spanning tree algorithm, proposed human body posture estimation algorithm for video, and RPC-based spanning tree model inference algorithm. We proposed a Markov chain Monte Carlo method based on 3D human motion silhouette projection library for monocular video images of the human body gesture tracking, motion capture equipment to get the basic movement of the body's appearance in a different library. Perspective projection of the human silhouette of clustering; using HOG monocular video images of the human body to detect the human body can be segmented more accurately the location of the body; the final adoption of the 3D silhouette model of the human body posture inference algorithm to analyze the model for each frame, re-use time constraints of the model to track the target. Constraint graph-driven MCMC using basic movements and combined to build a database for video data modeling and data-driven model is applied to the online behavior recognition; improve the body posture of the modeling capabilities.