Autonomous Virtual Agents for Performance Evaluation of Tracking Algorithms

This paper describes a framework which exploits the use of computer animation to evaluate the performance of tracking algorithms. This can be achieved in two different, complementary strategies. On the one hand, augmented reality allows to gradually increasing the scene complexity by adding virtual agents into a real image sequence. On the other hand, the simulation of virtual environments involving autonomous agents provides with synthetic image sequences. These are used to evaluate several difficult tracking problems which are under research nowadays, such as performance processing long---time runs and the evaluation of sequences containing crowds of people and numerous occlusions. Finally, a general event---based evaluation metric is defined to measure whether the agents and actions in the scene given by the ground truth were correctly tracked by comparing two event lists. This metric is suitable to evaluate different tracking approaches where the underlying algorithm may be completely different.