Karpuzov, S.; Petkov, G.H.; Ilieva, S.; Petkov, A.G.; Kalitzin, S.N. Remote Object Tracking Based on Optical Flow Reconstruction of Motion-Group Parameters. Preprints2024, 2024030747. https://doi.org/10.20944/preprints202403.0747.v1
APA Style
Karpuzov, S., Petkov, G.H., Ilieva, S., Petkov, A.G., & Kalitzin, S.N. (2024). Remote Object Tracking Based on Optical Flow Reconstruction of Motion-Group Parameters. Preprints. https://doi.org/10.20944/preprints202403.0747.v1
Chicago/Turabian Style
Karpuzov, S., Alexander Georgiev Petkov and Stiliyan Nikolov Kalitzin. 2024 "Remote Object Tracking Based on Optical Flow Reconstruction of Motion-Group Parameters" Preprints. https://doi.org/10.20944/preprints202403.0747.v1
Abstract
Abstract: Rationale. Object tracking has significance in many applications ranging from control of unmanned vehicles to autonomous monitoring of specific situations and events, especially when providing safety for patients with certain adverse conditions such as epileptic seizures. Conventional tracking methods face many challenges, such as the need for dedicated attached devices or tags, influence by high image noise, complex object movements, and intensive com-putational requirements. We have developed earlier computationally efficient algorithms for global optical flow reconstruction of group velocities that provide means for convulsive seizure detection and have potential applications in fall and apnea detection. Here, we address the chal-lenge of using the same calculated group velocities for object tracking in parallel. Methods. We propose a novel optical flow-based method for object tracking. It utilizes real-time image se-quences from the camera and directly reconstructs global motion-group parameters of the con-tent. These parameters can steer a rectangular region of interest surrounding the moving object to follow the target. The method successfully applies to multi-spectral data, further improving its effectiveness. Results. Experimental results on simulated tests and complex real-world data demonstrate the method's capabilities. The proposed optical flow reconstruction can provide accurate, robust, and faster results compared to current state-of-the-art approaches.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
