Limb Detection and Classification using Computer Vision and Machine Learning

This research delves into the development of a limb detection system that harnesses the power of contemporary computer vision and machine learning techniques. The primary objective of this study is to improve the accuracy and reliability of human limb recognition within video data, an area with significant implications for various fields such as healthcare, sports analytics, and interactive media in the near future. The study presents a detailed comparative analysis of several machine learning models, including Logistic Regression, Ridge Classifier, Random Forest, and Gradient Boosting. Among these, the Random Forest model emerged as the most promising, demonstrating superior performance in limb detection tasks. Our findings from this research highlights the potential for these models that can be applied in real-world scenarios, where precise motion detection is crucial. This paper not only contributes to the current body of knowledge by offering a comprehensive evaluation of these models but also lays the groundwork for future research and development in automated motion detection systems. The implications of our research can extend beyond academic inquiry, suggesting practical applications that could revolutionize industries dependent on accurate human motion analysis. This study also underscores the importance of model selection and hyperparameter tuning in achieving optimal performance, providing a foundation for further advancements in the field of computer vision and machine learning.