Article

Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking

School of Information Science and Engineering, Central South University, Changsha 410083, People's Republic of China
Pattern Recognition (Impact Factor: 3.1). 07/2009; 42(7):1559-1571. DOI: 10.1016/j.patcog.2008.12.024
Source: DBLP

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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    • "Studying how humans move and interact with their environment is an important part of Pervasive Health, AmI and Ubiquitous applications, like the telerehabilitation [2]. Thus, many efforts are being put to analyze human motion using different means (inertial sensors [3,4], camera-surveilled environments [5], a combination of both vision and inertial sensing [6], or robots following persons [7]). Monitoring human motion using cameras has shown to be very effective in representing motion characteristics, but presents issues with privacy and limitation of its application to closed spaces. "
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    ABSTRACT: Determination of (in)activity periods when monitoring human body motion is a mandatory preprocessing step in all human inertial navigation and position analysis applications. Distinction of (in)activity needs to be established in order to allow the system to recompute the calibration parameters of the inertial sensors as well as the Zero Velocity Updates (ZUPT) of inertial navigation. The periodical recomputation of these parameters allows the application to maintain a constant degree of precision. This work presents a comparative study among different well known inertial magnitude-based detectors and proposes a new approach by applying spectrum-based detectors and memory-based detectors. A robust statistical comparison is carried out by the use of an accelerometer and angular rate signal synthesizer that mimics the output of accelerometers and gyroscopes when subjects are performing basic activities of daily life. Theoretical results are verified by testing the algorithms over signals gathered using an Inertial Measurement Unit (IMU). Detection accuracy rates of up to 97% are achieved.
    Full-text · Article · Dec 2012 · Sensors
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    • "Based on the type of footage used, the markerless pose reconstruction (or motion capture) problem can be roughly categorized into two groups [24]: using video sequences from one camera or using footage from multiple calibrated cameras. Pose estimation from monocular video sequences [2], [3], [24], [17], [1], [18] can be more convenient for some applications as it imposes less restrictions to the user, but it has an inherent depth ambiguity. This ambiguity can be solved using structure from motion approaches, a very difficult problem in vision [13], [14]. "
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    ABSTRACT: We propose a data-driven, multi-view body pose estimation algorithm for video. It can operate in uncontrolled environments with loosely calibrated and low resolution cameras and without restricting assumptions on the family of possible poses or motions. Our algorithm first estimates a rough pose estimation using a spatial and temporal silhouette based search in a database of known poses. The estimated pose is improved in a novel pose consistency step acting locally on single frames and globally over the entire sequence. Finally, the resulting pose estimation is refined in a spatial and temporal pose optimization consisting of novel constraints to obtain an accurate pose. Our method proved to perform well on low resolution video footage from real broadcast of soccer games.
    Full-text · Conference Paper · Jan 2011
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    • "Inverse kinematics approaches can solve the body posture from their 3D position if we can clearly locate visible body parts such as face and hands. For example, in the work of Zou et al. [3], the angles of joints are estimated by inverse kinematics based on human skeleton constraints, and the coordinates of pixels in the body segments in the scene are determined by forward kinematics. Finally the human motion pose can be reconstructed by histogram matching. "
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    ABSTRACT: In order to study human motion in biomechanical applications, a critical component is to accurately obtain the 3D joint positions of the user's body. Computer vision and inverse kinematics are used to achieve this objective without markers or special devices attached to the body. The problem of these systems is that the inverse kinematics is “blinded” with respect to the projection of body segments into the images used by the computer vision algorithms. In this paper, we present how to add image constraints to inverse kinematics in order to estimate human motion. Specifically, we explain how to define a criterion to use images in order to guide the posture reconstruction of the articulated chain. Tests with synthetic images show how the scheme performs well in an ideal situation. In order to test its potential in real situations, more experiments with task specific image sequences are also presented. By means of a quantitative study of different sequences, the results obtained show how this approach improves the performance of inverse kinematics in this application.
    Full-text · Article · Jan 2010 · EURASIP journal on advances in signal processing
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