Visual object tracking is a crucial field in computer vision that involves tracking the movement of target objects in images or videos, predicting their position and other relevant information. This technique has numerous applications in various industries such as surveillance, retail, autonomous vehicles, and healthcare. Object tracking algorithms can be categorized into different types based on the task and type of inputs they are trained on, including image tracking, video tracking, single object tracking, and multiple object tracking. Traditional machine learning algorithms have been used for object tracking, but deep learning algorithms have proven to achieve success in recent years due to their ability to extract features and representations automatically. Deep learning algorithms such as DeepSORT, MDNet, SiamMask, and GOTURN have been trained on publicly available datasets and have shown promising results in object tracking tasks. These algorithms can be used for real-time object tracking and can operate at high speeds, making them suitable for applications that require fast processing. The choice of algorithm depends on the specific use case and the type of objects being tracked. To build an efficient object tracking algorithm, it is essential to have a strong annotated dataset, which can be created using platforms such as Encord.