tractCat

tractCat is a research project focused on developing and evaluating methods for tracking objects across multiple camera views. The project aims to improve the accuracy and robustness of multi-camera object tracking systems, particularly in complex and dynamic environments. Key aspects of tractCat involve the integration of appearance-based features with motion models to associate object detections over time and across different viewpoints. Researchers have explored various techniques, including deep learning-based feature extraction and advanced data association algorithms. The project often releases datasets and code to facilitate further research in the field. tractCat's contributions are relevant to applications such as surveillance, autonomous driving, and human-computer interaction, where understanding object trajectories in a wider scene is crucial. The project emphasizes the challenges of occlusions, varying object appearances, and the need for efficient computation in real-world scenarios.