ARTICLE SWV00/IDIAP Combining multiple tracking algorithms for improved general performance Shearer, Kim Wong, Kirrily D Venkatesh, Svetha EXTERNAL http://publications.idiap.ch/attachments/reports/2000/rr00-13.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/swv00tr Related documents Pattern Recognition 34 06 2000 Automated tracking of objects through a sequence of images has remained one of the difficult problems in computer vision. Numerous algorithms and techniques have been proposed for this task. Some algorithms perform well in restricted environments, such as tracking using stationary cameras, but a general solution is not currently available. A frequent problem is that when an algorithm is refined for one application, it becomes unsuitable for other applications. This paper proposes a general tracking system based on a different approach. Rather than refine one algorithm for a specific tracking task, two tracking algorithms are employed, and used to correct each other during the tracking task. By choosing the two algorithms such that they have complementary failure modes, a robust algorithm is created without increased specialisation. REPORT SWV00TR/IDIAP Combining multiple tracking algorithms for improved general performance Shearer, Kim Wong, Kirrily D Venkatesh, Svetha EXTERNAL http://publications.idiap.ch/attachments/reports/2000/rr00-13.pdf PUBLIC Idiap-RR-13-2000 2000 IDIAP Automated tracking of objects through a sequence of images has remained one of the difficult problems in computer vision. Numerous algorithms and techniques have been proposed for this task. Some algorithms perform well in restricted environments, such as tracking using stationary cameras, but a general solution is not currently available. A frequent problem is that when an algorithm is refined for one application, it becomes unsuitable for other applications. This paper proposes a general tracking system based on a different approach. Rather than refine one algorithm for a specific tracking task, two tracking algorithms are employed, and used to correct each other during the tracking task. By choosing the two algorithms such that they have complementary failure modes, a robust algorithm is created without increased specialisation.