3D Human motion tracking by exemplar-based conditional particle filter

Liu, Jigang; Liu, Dongquan; Dauwels, Justin; Seah, Hock Soon

doi:10.1016/j.sigpro.2014.08.028

Cited by 18 publications

(7 citation statements)

References 35 publications

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“…Second, the human movement must be faced to the camera. Furthermore, Liu et al [17] presented a full-body human motion tracking system using exemplar-based conditional particle filter. Similarly, Spruyt et al [18], presented an unsupervised method to automatically learn the context in which a hand was placed.…”

Section: Review Of Limb Motion Tracking Systemsmentioning

confidence: 99%

Real-Time Automated Contour Based Motion Tracking Using a Single-Camera for Upper Limb Angular Motion Measurement

Akhavizadegan

Idris

Razak

et al. 2020

MJCS

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Upper limb angular motion measurement is particularly a subject of interest in the field of rehabilitation. It is commonly measured manually by physiotherapist via goniometer to monitor stroke patient’s progress. However, manual measurement has many inherent drawbacks such as the need to have both patient and physiotherapist to be at the same place. In this paper, an automated real-time single-camera upper limb angular motion measuring system is proposed for home-based rehabilitation to aid physiotherapist to monitor patient’s progress. The measurement of concern are angle measurement of elbow extension, elbow flexion, wrist flexion and wrist extension. To do this, we propose a method that utilized predefined coordinate points extracted from the contours of the object named as contour based motion tracking. The proposed method overcome problems of prior target tracking techniques such as Kalman filter, Optical flow and Cam-shift. The proposed method includes skin region segmentation and arm modelling for motion tracking. Prior skin segmentation techniques suffer from fixed threshold value set by the user. Therefore, we introduce dynamic threshold based on the lower and upper threshold boundary of isolated skin regions from the background. To ensure the reliability of our skin segmentation method, we compare them with four different related algorithms. The result shows that our skin segmentation method outperformed the prior method with 93% detection accuracy. Following the segmentation, we model the arm motion tracking by formulating mathematical equation of various points and motion velocities to track the arm. We then model the wrist and elbow position to estimate arm angular motion. The method is put together and tested with real human subject and other test settings. The result shows that our proposed method able to produce an accurate and reliable reading of +-1.25 average range of error from actual physiotherapist reading.

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Section: Review Of Limb Motion Tracking Systemsmentioning

confidence: 99%

Real-Time Automated Contour Based Motion Tracking Using a Single-Camera for Upper Limb Angular Motion Measurement

Akhavizadegan

Idris

Razak

et al. 2020

MJCS

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“…In [40], a Bayesian filtering framework has been presented, which fuses point cloud information from an RGB-D camera with tactile information from a contact sensor, to track objects inhand manipulated by the robot. Bayesian networks have also been successfully used in [41][42][43][44] to track human postures. However, to our knowledge, no paper from the literature uses Bayesian networks to track human postures in the application of robot-assisted dressing.…”

Section: B Human Posture Trackingmentioning

confidence: 99%

Probabilistic Real-Time User Posture Tracking for Personalized Robot-Assisted Dressing

2019

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Robotic solutions to dressing assistance have the potential to provide tremendous support for elderly and disabled people. However, unexpected user movements may lead to dressing failures or even pose a risk to the user. Tracking such user movements with vision sensors is challenging due to severe visual occlusions created by the robot and clothes. We propose a probabilistic tracking method using Bayesian networks in latent spaces, which fuses robot end-effector positions and force information to enable camera-less and real-time estimation of the user postures during dressing. The latent spaces are created before dressing by modeling the user movements with a Gaussian Process Latent Variable Model, taking the user's movement limitations into account. We introduce a robot-assisted dressing system that combines our tracking method with hierarchical multi-task control to minimize the force between the user and the robot. The experimental results demonstrate the robustness and accuracy of our tracking method. The proposed method enables the Baxter robot to provide personalized dressing assistance in putting on a sleeveless jacket for users with (simulated) upperbody impairments.

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“…Li et al [36] use a Mixture of Factor Analyzers to transform high-dimensional poses into a Globally Coordinated Local Linear Models. Liu et al [37] propose an exemplar-based conditional particle filter (EC-PF) from monocular camera by introducing a conditional term with respect to exemplars and image data. Chang and Lin [38] propose a novel progressive particle filter comprises three principal techniques: hierarchical searching, multiple predictions, and iterative mode-seeking.…”

Section: Related Workmentioning

confidence: 99%

An Image-Constrained Particle Filter for 3D Human Motion Tracking

et al. 2019

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Tracking 3D human motion from monocular video sequences has aroused great interest in recent years. Among these human motion tracking methods, the particle filter is considered as an effective approach. However, the current approaches based on particle filter still have some limitation such as many particles are obviously not consistent with the observed image due to they are independent of the image information. In this paper, we present an image-constrained particle filter approach to track 3D human motion from monocular video clips with the assistance of a pre-captured motion library. We propose two novel particle filtering criteria and design a hierarchical likelihood function. The top layer of the function consists of the particle filtering criteria, and the bottom layer consists of the likelihood functions based on image contours and edge features. We remove those particles that do not match the image significantly at the top level, and the remaining particles are evaluated using the underlying likelihood function. The experimental results show that our method can effectively improve the accuracy of motion tracking and constrain the estimation of human body position. INDEX TERMS 3D human motion tracking, image constraint, particle filter, monocular video.

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3D Human motion tracking by exemplar-based conditional particle filter

Cited by 18 publications

References 35 publications

Real-Time Automated Contour Based Motion Tracking Using a Single-Camera for Upper Limb Angular Motion Measurement

Real-Time Automated Contour Based Motion Tracking Using a Single-Camera for Upper Limb Angular Motion Measurement

Probabilistic Real-Time User Posture Tracking for Personalized Robot-Assisted Dressing

An Image-Constrained Particle Filter for 3D Human Motion Tracking

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