@article{4704,
  author = {Hathairat Ketmaneechairat},
  title = {Fusion Mean Shift-SIFT Tracking Framework with Quaternion- Based Skeletal Modeling for Robust Sports Motion Trajectory Capture},
  journal = {Progress in Machines and Systems},
  year = {2026},
  volume = {15},
  number = {1},
  doi = {https://doi.org/10.6025/pms/2026/15/1/1-18},
  url = {https://www.dline.info/pms/fulltext/v15n1/pmsv15n1_1.pdf},
  abstract = {Accurate acquisition of athletes' motion trajectories under high-speed, complex environmental conditions
remains a major challenge in sports biomechanics and intelligent training analysis. This study proposes a
framework for capturing Games movement trajectories using the mean shift algorithm to address limitations
of conventional tracking methods in dynamic training environments. A skeletal representation consisting of
16 joints and 51 degrees of freedom is constructed to model human motion during Games training. Quaternionbased
joint representation and logarithmic mapping are employed to reduce rotational singularities and
enable dimensionality reduction within a linear time-invariant system. To reduce environmental parameter
dependency, the probability density function in the gradient iteration framework is integrated with kernel
density estimation, while colour histograms are used as stable visual features for target localisation.
Additionally, Difference of Gaussian (DoG)-based feature detection and SIFT-assisted fusion tracking are
introduced to enhance robustness under illumination variation and motion blur. Experimental validation
demonstrates improved detection accuracy, reduced false alarm rates, and superior clustering efficiency
compared with conventional estimation-based tracking approaches. The proposed framework provides a
reliable, adaptive, and computationally efficient solution for acquiring Games motion trajectories without
requiring external parameter tuning},
}