Object tracking is a computer vision technique used to locate and follow the movement of an object (or multiple objects) over time in a sequence of images or video frames. The process begins by detecting the object of interest in the initial frame, after which a tracking algorithm estimates its position in subsequent frames. This technique is essential for applications such as video surveillance, autonomous vehicles, human-computer interaction, and augmented reality, where it is important to maintain awareness of object movement and behavior over time.
There are several approaches to object tracking, including correlation-based, feature-based, and deep learning-based methods. Traditional methods like Kalman filters and Mean-Shift focus on motion prediction and object appearance, while more recent deep learning models leverage convolutional neural networks (CNNs) to learn object representations more robustly. Challenges in object tracking include occlusion, changes in lighting, fast motion, and object deformation, which can significantly affect accuracy. Despite these challenges, advancements in hardware and algorithms continue to improve the reliability and efficiency of object tracking systems.