Great technological advances in the last decades have enabled the creation of multimedia systems that capture high resolution images at high frame rates. As more and more digital video content is generated, we strive for ways to automatically analyze and understand video through computer vision, for categorization, searchable video and other purposes. Object recognition forms an important part in this evolution, where objects in video are found and identified. In this thesis, we introduce a real-time distributed video pipeline that produces high resolution panorama video. Created as a diverse tool for capture and analysis of sports footage, the system allows for real-time generation and viewing of a cylindrical panorama covering an entire soccer field using five industrial cameras. In three case studies, we investigate the potential for ball and object tracking within such a panorama, by installing the system in both indoor and outdoor locations using different configurations. By selecting three current state-of-the-art object tracking algorithms and through implementation of three of our own tracker algorithms, we evaluate the performance and feasibility of object tracking in a real-time video pipeline. We detail and evaluate the performance of these six trackers in all case studies, and learn that there is often a trade-off between tracking robustness and execution speed. Trackers that maintain adaptive appearance models to track unknown objects, struggle with handling the workload of the high resolution video produced by Bagadus. By developing tracking algorithms specific to each sequence, we are able to achieve usable performance by reducing computations, at the sacrifice of portability. We recognize that especially ball tracking in team sports is a very challenging case, and hope to provide knowledge useful for further research.