Machine learning is continually evolving, leading to advancements in translating and interpreting various writing systems. Numerous studies have aimed to assist learners and researchers in this field, primarily focusing on image processing techniques to recognize and translate Baybayin characters from pre-written inputs. However, there is limited research on utilizing optical flow methods for capturing stroke movements in real-time handwriting applications. This study addresses this gap by investigating the effectiveness of the Lucas-Kanade optical flow method for tracking and translating drawn Baybayin characters into their respective scripts. The developed system captures user-drawn characters through a Raspberry Pi camera, employing the Lucas-Kanade algorithm to convert motion patterns into usable data inputs. The study integrates Optical Character Recognition (OCR) and a Recurrent Neural Network (RNN) to facilitate translation. A confusion matrix was used to evaluate the system's accuracy, resulting in an impressive rate of 87.5%. These findings indicate that Lucas-Kanade optical flow is a viable approach for translating single-stroke handwritten Baybayin characters.

This study investigates the accuracy of optical flow algorithms-Lucas-Kanade, Farneback, and Horn-Schunck-in motion analysis applications. Significant differences in tracking success rates among the algorithms were analyzed using the chi-square test of independence. Lucas-Kanade demonstrated the highest observed success rate with 87 out of 99 tracked pedestrians, followed by Farneback and Horn-Schunck, each with 84 out of 99 tracked pedestrians. The calculated chi-square statistic of 6.00 exceeded the critical value - 5.991, where the significance level is set at 0.05, which led to the rejection of the null hypothesis. Results indicate a statistically significant association between the algorithms' tracking accuracy and their observed success rates. These findings underscore Lucas-Kanade's advantage in achieving superior tracking accuracy under controlled experimental conditions. The importance of algorithm selection based on the requirements of the specific application is emphasized in the study, highlighting Lucas-Kanade's potential for optimizing motion analysis systems in domains such as robotics, autonomous vehicles, and surveillance. Future research could explore further optimizations to enhance algorithm performance across diverse environmental conditions and motion complexities.

A robust pedestrian tracking system using YOLOv8 for detection and Lucas-Kanade optical flow for motion tracking under varying lighting conditions is developed. Pedestrian tracking is essential for surveillance, robotics, and autonomous vehicles, requiring accurate detection in dynamic environments. A Raspberry Pi camera is employed for real-time processing, and the system's performance is evaluated in artificial and real-world lighting scenarios. Results show a chi-square statistic value - 0.081, significantly lesser than the critical value of 5.991. Results suggests - no significant association exists between the lighting conditions. The results demonstrate effective pedestrian tracking capabilities, highlighting the system's potential to enhance pedestrian safety and traffic management. A significant challenge to be addressed is mitigating human occlusion, which is a primary problem for tracking accuracy in crowded or obstructed environments. Future research should focus on advanced techniques to minimize occlusion effects, ensuring reliable performance across diverse real-world conditions and validating their practical applicability.