Multi Camera Tracking Dataset

Multi-camera tracking datasets are crucial for developing and evaluating algorithms that track multiple objects across multiple, overlapping video cameras. Current research focuses on creating larger, more realistic datasets, often employing semi-automatic annotation systems to reduce the substantial manual effort involved in labeling ground truth trajectories. These datasets are vital for advancing deep learning-based multi-target, multi-camera tracking (MTMCT) models, which struggle with real-world challenges like occlusions and long-term tracking, and are essential for improving applications such as traffic monitoring and security surveillance. The availability of high-quality, publicly available datasets is a key factor limiting the progress of the field.