From image sequences towards conceptual descriptions
Image and Vision Computing
Autonomous virtual agents learning a cognitive model and evolving
Lecture Notes in Computer Science
Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Evaluation of Motion Segmentation Quality for Aircraft Activity Surveillance
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
PETS Metrics: On-Line Performance Evaluation Service
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A New Evaluation Approach for Video Processing Algorithms
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Robust Multiple-People Tracking Using Colour-Based Particle Filters
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
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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.