Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking
ABSTRACT We present a method to reconstruct human motion pose from uncalibrated monocular video sequences based on the morphing appearance model matching. The human pose estimation is made by integrated human joint tracking with pose reconstruction in depth-first order. Firstly, the Euler angles of joint are estimated by inverse kinematics based on human skeleton constrain. Then, the coordinates of pixels in the body segments in the scene are determined by forward kinematics, by projecting these pixels in the scene onto the image plane under the assumption of perspective projection to obtain the region of morphing appearance model in the image. Finally, the human motion pose can be reconstructed by histogram matching. The experimental results show that this method can obtain favorable reconstruction results on a number of complex human motion sequences.
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ABSTRACT: The newly developed enhanced hexagonal-based search using point-oriented inner search (EHS-POIS) enormously speeds up hexagon-based search (HS). From a different perspective, an inherent correlation between distortion and spatial direction through statistical analysis is found. Based on the observed distortion distribution, a novel enhanced hexagonal-based search with direction-oriented inner search (EHS-DIOS) is proposed to avoid real distortion calculation and thus reduce high computation. Experimental results show that, the proposed algorithm is faster than EHS-POIS by achieving two times improvement in terms of inner search speed, and as compared with previous works, it makes a better tradeoff between speed and decoded image quality.IEEE Transactions on Circuits and Systems for Video Technology 02/2010; · 2.26 Impact Factor
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ABSTRACT: This paper presents a novel solution to the problem of tracking the 3D position, orientation and full articulation of a human hand from single depth images. We choose the model-based approach and treat the tracking task as an optimization problem. A new objective function based on depth information is presented to quantify the discrepancy between the appearance of hypothesized instances of a hand model and actual hand observations. Sequential Particle Swarm Optimization method is proposed to minimize the objective function for sequential optimization. An semi-automatic hand location method is adopted to predict hand region for sequential tracking. A GPU-based implementation of the proposed method is well designed to address the computational intensity. Extensive experimental results demonstrate qualitatively and quantitatively that tracking of an articulated hand can be achieved in real-time.Pattern Recognition Letters 09/2013; 34(12):1437–1445. · 1.06 Impact Factor
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ABSTRACT: In wireless body sensor networks, sensors may be installed on various body limbs to wirelessly collect body information for homecare services. The orientations and accel-erations on each limb are different for various motion states. For example, each limb has different acceleration when walking versus running, and orientation when standing versus lying. According to the above information, the body motion state may be decided. Furthermore, each person has unique body characteristics such as height, foot pitch, and motion habit to effect the body reconstruction. Therefore, it is a challenging issue how to present human motions through 3D skeleton system simulation, and achieve an adaptive reconstruction of human motion according to the different body characteristics of each person. In this study, we proposed a novel scheme to utilize multiple triple axis accelerometer and gyroscopes to measure limb accelerations, then calculated the locations of limbs and try to employ kinematic theory to reconstruct human body skeleton, called 3D Adaptive human Motion Reconstruction (AMR). And we applied Body Correction Algorithm (BCA) to correct human body characteristics and fighted the error of transmission noise. This system was tested and validated with success.