Archives for Technical Sciences | 2026
Authors: Nife N.I.; Nihad M.; Hussein M.A.; Omar H.K.
DOI: 10.70102/afts.2026.1835.188
Journal: Archives for Technical Sciences
Year: 2026
Publisher: Technical institute of Bijeljina
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
Computer vision is a significant field of application of mathematical models developed to monitor 3D anatomical locations of the human body, particularly in robotics, surveillance, and medicine. In this paper, we present a new model that applies the Kalman filter (KF) to track 3D anatomical features in real time with increased precision. The approach separates video frames, identifies objects using the pixel characteristics, and uses the Kalman filter to forecast and correct landmark locations. As shown, experimental results demonstrate that the proposed solution is much more efficient than conventional tracking mechanisms. The system's tracking accuracy has improved, with a mean squared error (MSE) of 0.035 compared to 0.048 for the baseline Kalman filter. The suggested approach provides a processing time of 15 ms per frame, which is sufficient for real-time operation. The fact that the system can cope with noisy data and any high-speed movements makes it robust and is applicable in cases where the application needs high precision and low latency. The test confirms that Kalman filtering and deep learning feature-extraction frameworks improve the tracking accuracy and stability of 3D anatomical landmarks in dynamic systems. This study opens the way to more trustworthy real-time medical surveillance, rehabilitation, and robotic interaction tracking solutions. © 2026, Technical institute of Bijeljina. All rights reserved.
3D anatomical landmark tracking; computer vision; human body tracking; kalman filter; motion prediction; object detection