Multi-object tracking (MOT) is a critical computer vision task used in fields like autonomous driving, sports analytics, and surveillance, involving the identification and tracking of multiple objects across video frames while maintaining their unique features. Challenges such as occlusion, motion blur, and annotation inconsistencies can impact the accuracy of MOT models, making high-quality data annotation essential for reliable tracking and reducing errors in downstream applications. The process of MOT includes object detection, feature extraction, and data association to maintain consistency across frames, with challenges like identity swaps and changes in object appearances requiring attention during annotation. Tools like Encord facilitate efficient MOT annotation by utilizing AI-assisted tracking, interpolation, and quality metrics to streamline the workflow, manage occlusions and complex motions, and ensure frame-by-frame consistency, ultimately reducing manual effort and enhancing the quality of datasets for model training in real-world applications.