Chengkai Wan

Better Foreground Segmentation for Static Cameras via New Energy Form and Dynamic Graph-cut

In this paper, we propose a new foreground segmentation method for applications using static cameras. It formulates foreground segmentation as an energy minimization problem, and produces much better results than conventional background subtraction methods. Due to the integration of better likelihood term, shadow elimination term and contrast term into energy function, it also achieves more accurate segmentation than existing method of the same type. Furthermore, real-time performance is made possible by employing dynamic graph-cut algorithm. Quantitative and qualitative experiments on real videos demonstrate our improvements

Markerless human body motion capture using Markov random field and dynamic graph cuts

Current vision-based human body motion capture methods always use passive markers that are attached to key locations on the human body. However, such systems may confront subjects with cumbersome markers, making it difficult to convert the marker data into kinematic motion. In this paper, we propose a new algorithm for markerless computer vision-based human body motion capture. We compute volume data (voxels) representation from the images using the method of SFS (shape from silhouettes), and consider the volume data as a MRF (Markov random field). Then we match a predefined human body model with pose parameters to the volume data, and the calculation of this matching is transformed into energy function minimization. We convert the problem of energy function construction into a 3D graph construction, and get the minimal energy by the max-flow theory. Finally, we recover the human pose by Powell algorithm.

A moving object segmentation algorithm for static camera via active contours and GMM

Moving object segmentation is one of the most challenging issues in computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Our algorithm integrates the GMM background model, shadow elimination term and curve evolution edge stopping term into energy function. It achieves more accurate segmentation than existing methods of the same type. Promising results on real images demonstrate the potential of the presented method.

Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints

Currently, many vision-based motion capture systems require passive markers attached to key locations on the human body. However, such systems are intrusive with limited application. The algorithm that we use for human motion capture in this paper is based on Markov random field (MRF) and dynamic graph cuts. It takes full account of the impact of 3D reconstruction error and integrates human motion capture and 3D reconstruction into MRF-MAP framework. For more accurate and robust performance, we extend our algorithm by incorporating color constraints into the pose estimation process. The advantages of incorporating color constraints are demonstrated by experimental results on several video sequences.

A new algorithm for static camera foreground segmentation via active coutours and GMM

Foreground segmentation is one of the most challenging problems in computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Because of the integration of GMM background model, shadow elimination term and curve evolution edge stopping term into energy function, it achieves more accurate segmentation than existing method of the same type. Promising results on real images demonstrate the potential of the presented method.

Model-Based Markerless Human Body Motion Capture using Multiple Cameras

Current markerless model-based human body motion capture methods always aim at accurate human body model and reconstruction surface contour. Unfortunately, because of the factors such as loose clothing, image noise and background segmentation errors, the efforts of these methods get very limited effects. In this paper, we propose a new algorithm for markerless model-based human body motion capture which is robust to the inaccurate human body reconstruction caused by the factors mentioned above. We extracted a volume data (voxel) representation from silhouettes in multiple video images. In the consideration of the human body model, we construct an articulated model with a potential energy which emphasize the skeleton of the human body and is not sensitive to the outline details of the human body surface. Then, we fit the human body model to the volume data in an expectation-maximization framework and recover the pose of the human body.

A New Algorithm for Human Motion Capture via 3D Active Contours

Motion capture is one of the most challenging problems in computer vision. In this paper, we propose a new algorithm for markerless human body motion capture. We compute volume data (voxels) representation from the images using the method of SFS (shape from silhouettes). Then we match a predefined human body model with pose parameter to the volume data, and the calculation of this matching is transformed into energy function minimization. In minimizing the energy function, we use a method of 3D active contours to solve this problem. In the process of curving surface evolution, the curving surface will drive the human model close to the visual hull. On the other hand, when the human model is superposed with the human real pose, the curving surface can create a 3D human body reconstruction based on the visual hull and human model. Promising results on real images demonstrate the potentials of the presented method.

Model-based markerless human body motion capture using active contour

In this paper, we propose a new markerless model-based human body motion capture algorithm. It no longer requires the foreground segmentation as an essential introductory step. The algorithm combines the human body model with pose parameters and the images from multiple cameras, and conducts segmentation and motion capture simultaneously via active contours and level set. In the process of curve evolution, the curve drives the human model close to the human real pose in each camera. On the other hand, when the human model is superposed with the human real pose, the curve will be balanced at the best position based on the prior shape segmentation. Promising results on real images demonstrate the potentials of the presented method.

Human Body Motion Capture via 3D Graph-cuts

In this paper, we propose a new algorithm for markerless vision-based human body motion capture. In our method, motion capture or pose estimation is considered as an energy minimization problem, and the energy function construction is converted to a 3D graph construction. During this procedure, a human model with a certain pose will be taken into account. At last Powell algorithm will be used to optimize the result of the energy function