Empirical Analysis of Machine Learning Models Used for Motion Detection & Tracking in Videos from An Augmented Statistical Perspective

Abstract

The goal of the current study is to carry out an empirical analysis of several machine learning models used for motion detection and tracking in video streams. The inquiry is conducted from a statistical perspective, and different motion detection and tracking approaches are systematically evaluated and contrasted. The careful assessment of these models' performance characteristics in this study, including measures for prediction accuracy, recall rates, temporal delay, and scalability, distinguishes it from others. This research helps to clarify the advantages and disadvantages of each model by methodically comparing various motion detection and tracking approaches. The empirical findings reported here provide a thorough understanding of the performance of these models in actual video scenarios. The ability of the models to accurately recognise and follow moving objects is demonstrated by the comparison of prediction accuracy and recall. Quantifying the temporal responsiveness of each methodology is accomplished through the evaluation of temporal delay. Additionally, the assessment of scalability measures gauges the models' ability to adapt to the changing complexity of video data samples. This study elucidates the complex interaction between the various machine learning models and their corresponding performance indicators by providing a detailed analysis of the many variables under examination. This empirical investigation improves their understanding of motion detection and tracking in videos and makes it easier to choose the right model for real-world applications. The results highlight the importance of thoroughly evaluating variables for predictive accuracy, recall potential, temporal delay, and scalability when implementing machine learning methods for motion detection and tracking inside video streams.