Research On Marker-less Human Body Motion Capture And Pose Estimation

Eighty percents of the information human get from outside are obtaioned through the vision.Let computer have human-like vision capability is a dream of researchers for many years.With the development of the human-computer interaction technology,the natural and mult-modal interaction between human and computer will become the main form of the interaction.But this requires the computer can capture and understand the behaviors of the human correctly.Motion capture is proposed under this situation.The goal of motion capture is to detect and record the motion and expression of moving objects,which can be represented as poses of the objects at any time,and then converted to abstract digital format.It is one of the key technologies of new generation of human-computer interaction. It also applies to the game production,sports analysis,virtual reality, intelligent control and model-based coding etc.Vision based human body motion capture has the merits of non-invasive, low cost,intelligence and so on.Recovering human body poses from image sequence has become one of the hotspots in motion capture research field. While because of the difficulties of non-rigid human body moving,2D-3D projection,self occlusion,occlusions and self- occlusions,high dimensionality of state space and image features extraction under clutter, it is a challenging task in the field of computer vision.In this dissertation,vision-based markerless motion capture is investigated.Main contributions of this thesis can be summarized as follows:1.We propose a multi-constraint active contours based method for moving object extraction(MC-GMM-Active Contours).It imports the GMM background model description under the framework of active contours.In order to get the information of target in former frame,we introduce the the foreground color model into the energy function,Diference from the past method,we use a shadow elimination term in energy function to inhibit the shadow instead of an independent module.People recognize objects, mainly from the outline of their shape.The curvature constraint in energy function will introduce the restriction of the target profile and a priori knowledge constraint into the moving object extraction.We use curve evolution and level set method to optimize the energy function.At last we use the unconditionally stable semi-implicit Additive Operator Splitting algorithm in level set numerical solution.All these make us can get the accurate extraction of moving object form static camera.2.A new pose estimation method combination of MRF and a new distance energy model is proposed.Compared to the past MRF-based Motion Capture algorithm,the method has the following improvements:In order to make the human model fit for the performer,we propose an adaptive distance energy model based on the skeleton model,it can be updated online according to the feedback in the process of pose estimation;we use a more effective binary interaction term in energy function according to the relationship between voxels.In order to restrict the rationality of the pose of human body,we introduce a more effective additional constraint in energy function.3.A 3D active contours based motion capture algorithm is proposed. This method no longer considers the moving object extraction as an independent module in metion capture.Previous methods of pose estimation work based on the target extraction result,and only deal with the image within the contour of target.Once there is any error in the target extraction step,the error cannot be rectified in the following steps.Our active contours based 3D human motion capture method seamlessly integrates the target extraction and pose estimation into the active contours framework,and combines the tasks of motion capture and 3D reconstruction. At the same time,the algorithm introduces a priori constraint of human motion to restrict the human pose.These improvements make our algorithm to get better effects than general algorithms.4.A 2D active contours based strong priors moving object segmentation and pose estimation method is proposed.In many cases,people not only want to get more accurate pose estimation result,they also want to get a better result of moving object segmentation.Therefore,this method is committed to get the body's pose and a better human body segmentation result simultaneously.This method still works based on the human body model,and combines the tasks of motion capture and moving object sengmentation under the active contours framework.On one hand,the strong prior can allow the moving object get better segmentation results.On the other hand,the better target segmentation also makes the pose estimation obtain higher precision.