Real Time Object Tracking Using OpenCV

Abstract

Object tracking has been widely used in the field of computer vision. The OpenCV Library offers a wide range of Target Tracing Algorithms, which have been shown to be robust and effective. But as technology advances, precision and robust can be improved. In this overview, we look at the latest developments and new developments in OpenCV's object tracking.The focus is to provide an overview of some of the latest methods used to improve object tracking accuracy and robustness. We discuss the strengths and weaknesses of each approach and evaluate their performance based on standard benchmarks datasets. From our analysis, we observe that deep learning-based methods, coupled with motion models, have recently demonstrated impressive results. We also highlight the limitations and challenges faced in real-world scenarios with complex environments, occlusions, and deformable objects. Finally, we discuss interesting future research directions, including exploring hybrid methods that combine several techniques to improve tracking accuracy and robustness. With the advent of IoT, drones, and autonomous vehicles, object tracking has become increasingly relevant in our daily lives, and thus, the pursuit of accurate and robust algorithms is as crucial as ever. In conclusion, this review provides insights into some of the recent advancements and challenges in object tracking in OpenCV. The review offers a valuable resource for researchers and practitioners working in the field and highlights gaps in current research that require further investigation.