View-invariant 3D human body pose reconstruction using a monocular video camera

Ke, Shian-Ru; Hwang, Jenq‐Neng; Lan, Kung-Ming; Wang, Shen-Zheng

doi:10.1109/icdsc.2011.6042900

Cited by 9 publications

(15 citation statements)

References 17 publications

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“…The objective of this phase is to extract the 3D coordinates of 13 human joints from monocular video sequences [25]. Three stages are in [25]: individual segmentation and 2D features extraction, 2D body parts tracking, and 3D pose estimation.…”

Section: D Human Pose Estimationmentioning

confidence: 99%

“…These five blobs are tracked frame-by-frame, based on shape, color, and temporal information. Three techniques -2D skeletonization scheme [25], mean-shift tracking algorithm [44]- [45], and Kalman filter prediction [46] -are separately applied to take advantage of the shape, color, and temporal information. The trajectories of the five blobs are shown in Fig.…”

Section: D Body Parts Trackingmentioning

confidence: 99%

“…Three stages are in [25]: individual segmentation and 2D features extraction, 2D body parts tracking, and 3D pose estimation.…”

Section: D Human Pose Estimationmentioning

confidence: 99%

“…3D human poses are inferred based on a method of Data-Driven Markov Chain Monte Carlo [22]- [23]; however, the computation cost is extremely high. Considering accuracy of pose estimation and time complexity simultaneously, Ke and others [24]- [25] propose a method to track 2D body parts by integrating shape, color, and temporal information to effectively estimate 3D human poses.…”

Section: Introductionmentioning

confidence: 99%

“…Then the human object's 3D poses are estimated by using the 3D coordinates of their body joints. This is all done based on a 3D human pose estimation technique [25]. In the second phase, the estimated 3D coordinates of the body joints are converted into one-dimensional feature vectors, based on GRF conversion [29] and k-means clustering [39].…”

Section: Introductionmentioning

confidence: 99%

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Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs

Thuc

Hwang

et al. 2014

ETRI J

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Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k-means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum-Welch re-estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems. Keywords I. IntroductionHuman action recognition is a growing topic in video analysis and understanding -one of the most popular areas in the community of computer vision -thanks to its applications in surveillance, entertainment, and healthcare. In surveillance, human action recognition can be used in conjunction with video camera footage to help with the recognition and analysis of human actions. In entertainment, human-computer interaction can be helped to appear more natural via human action recognition, which in turn can help increase the entertainment experience. In healthcare, human action recognition can help detect abnormal gaits or assist in a patient's rehabilitation through an analysis of their actions.However, it is challenging to recognize various human actions due to the high number of degrees of freedom associated with the average human body -namely, variations in human poses; variations in the colors of a person's clothing; changes in lighting and illumination; variations in viewpoints; and frequent self-occlusion. Moreover, the use of monocular video sequences further increases the difficulty for human action recognition.Generally, the two main stages in human action recognition are: the feature extraction and representation stage and the classification stage.In the feature extraction and representation stage, the features or characteristics of video frames, such as silhouette, shape, color, and motion, are extracted and represented in a systematic and efficient way. consists of stacking segmented silhouettes (frame by frame) to form a 3D spatial-temporal shape. In a similar way, Ke and others [2] build STVs, for shape-based matching, from image features that are based on the consecutive silhouettes of objects along a time axis, including spatial-temporal region extraction and region matching. Kim and oth...

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Section: D Human Pose Estimationmentioning

confidence: 99%

Section: D Body Parts Trackingmentioning

confidence: 99%

“…Three stages are in [25]: individual segmentation and 2D features extraction, 2D body parts tracking, and 3D pose estimation.…”

Section: D Human Pose Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs

Thuc

Hwang

et al. 2014

ETRI J

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Contextualised learning‐free three‐dimensional body pose estimation from two‐dimensional body features in monocular images

Unzueta

Aranjuelo

Goenetxea

et al. 2016

IET Computer Vision

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In this study, the authors present a learning‐free method for inferring kinematically plausible three‐dimensional (3D) human body poses contextualised in a predefined 3D world, given a set of 2D body features extracted from monocular images. This contextualisation has the advantage of providing further semantic information about the observed scene. Their method consists of two main steps. Initially, the camera parameters are obtained by adjusting the reference floor of the predefined 3D world to four key‐points in the image. Then, the person's body part lengths and pose are estimated by fitting a parametrised multi‐body 3D kinematic model to 2D image body features, which can be located by state‐of‐the‐art body part detectors. The adjustment is carried out by a hierarchical optimisation procedure, where the model's scale variations are considered first and then the body part lengths are refined. At each iteration, tentative poses are inferred by a combination of efficient perspective‐n‐point camera pose estimation and constrained viewpoint‐dependent inverse kinematics. Experimental results show that their method obtains good results in terms of accuracy with respect to state‐of‐the‐art alternatives, but without the need of learning 2D/3D mapping models from training data. Their method works efficiently, allowing its integration in video soft sensing systems.

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Quasi-periodic action recognition from monocular videos via 3D human models and cyclic HMMs

Thuc

Hwang

et al. 2012

The 2012 International Conference on Advanced Technologies for Communications

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This paper proposes a system to recognize quasiperiodic human actions from monocular video sequences. First, each input video frame is analyzed and estimated to generate the best 3D human model pose which consists of a set of 3D coordinates of specific human joints. ext, these 3D coordinates for each frame are converted into corresponding 3D geometric relational features (GRFs), which describe the geometric relations among body joints of a pose. Finally, we train a cyclic hidden Markov model (CHMM) for each action based on the vector quantized 3D GRFs, and the trained CHMMs are used to classify different quasi-periodic human actions. The experimental results indicate the effectiveness of the proposed system in terms of the view point invariance, the low-dimensional feature vectors, and the encouraging recognition rates.

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View-invariant 3D human body pose reconstruction using a monocular video camera

Cited by 9 publications

References 17 publications

Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs

Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs

Contextualised learning‐free three‐dimensional body pose estimation from two‐dimensional body features in monocular images

Quasi-periodic action recognition from monocular videos via 3D human models and cyclic HMMs

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