Skeleton-based viewpoint invariant transformation for motion analysis

Han, Yu; Chung, Sheng Luen; Yeh, J.; Chen, Qi Jun

doi:10.1117/1.jei.23.4.043021

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…This paper utilizes SVIT presented in [9] to eliminate viewpoint variance, builds two types of features, uses ELM as the trainer, and meanwhile, presents feature fusion to improve recognition precision.…”

Section: Methodsmentioning

confidence: 99%

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Activity Recognition Based on Relative Positional Relationship of Human Joints

Han

Chung

2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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Kinect have been used as a revolutionary sensor for recent human activity recognition research, mainly due to its ready skeletal joint information that facilitates activity analysis. However, the sensor's unstable and imprecise measurement may impair the analysis results. To alleviate this impact due to the unavoidable noisy measurement, this paper presents a recognition approach based on the relative positional relationship among measured joints data. Relative positional relationship between two joints refers to the relation either one joint's position in the x, y, z coordinates is to the left or right, above or below, in front or behind the other. Capturing these relative positional relationship among all measured joints data as a {-1, 0, 1} sequence throughout the time span of an activity, or more succinctly represented by a summary of the change frequency of these relationship, proves to be an effective feature for comparison and classification. In doing so, measured joint data are first pre-processed by SVIT (skeletonbased viewpoint invariant transformation) to eliminate viewpoint variation. Then, as the first feature, change frequencies of relative positional relationship among joints are constructed. As a complimentary feature, a HOG3D-like feature that summarizes the joints spatial distribution throughout an activity is also constructed. Classification credibility of feature is then applied to fuse the two features with ELM as machine learning for activity classification. The proposed approach with its efficient implementation has been applied to MSRDailyActivity3D and demonstrated favorable performance than results from literature.

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Section: Methodsmentioning

confidence: 99%

“…To be more precise, the relative positional relationship refers to the relation either one joint's position in the x, y, z coordinates is to the left or right, above or below, in front or behind the other. In doing so, SVIT presented in [9] is used as preprocessing to eliminate viewpoint variance.…”

Section: A Feature Constructionmentioning

confidence: 99%

Activity Recognition Based on Relative Positional Relationship of Human Joints

Han

Chung

2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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“…A comparison regarding the accuracy of joint positions and bone lengths for the first and second generation of the Microsoft Kinect has been conducted by Wang et al [11]. Han et al [12] put forward a skeleton-based viewpoint invariant transformation to map 3-D skeleton data to an orthogonal coordinate system built by the left and right shoulders and the spine.…”

Section: Related Workmentioning

confidence: 99%

Constraint-Based Optimized Human Skeleton Extraction from Single-Depth Camera

Weinmann

et al. 2019

Sensors

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As a cutting-edge research topic in computer vision and graphics for decades, human skeleton extraction from single-depth camera remains challenging due to possibly occurring occlusions of different body parts, huge appearance variations, and sensor noise. In this paper, we propose to incorporate human skeleton length conservation and symmetry priors as well as temporal constraints to enhance the consistency and continuity for the estimated skeleton of a moving human body. Given an initial estimation of the skeleton joint positions provided per frame by the Kinect SDK or Nuitrack SDK, which do not follow the aforementioned priors and can prone to errors, our framework improves the accuracy of these pose estimates based on the length and symmetry constraints. In addition, our method is device-independent and can be integrated into skeleton extraction SDKs for refinement, allowing the detection of outliers within the initial joint location estimates and predicting new joint location estimates following the temporal observations. The experimental results demonstrate the effectiveness and robustness of our approach in several cases.

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