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Multi-Object Tracking driven Event Detection for Evaluation

D. Roth, E. Koller-Meier and L. Van Gool
AREA workshop @ ACM Multimedia
October 2008


This paper describes a monocular object tracker, able to detect and track multiple object classes in non-controlled environments. Our tracking framework uses Bayesian per-pixel classification to segment an image into foreground and background objects, based on observations of object appearances and motions in real-time. Furthermore, semantically high level events are automatically extracted from the tracking data for performance evaluation. The reliability of the event detection is demonstrated by applying it to state-of-the-art methods and comparing the results to human annotated ground truth data for multiple public datasets.

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  author = {D. Roth and E. Koller-Meier and L. Van Gool},
  title = {Multi-Object Tracking driven Event Detection for Evaluation},
  booktitle = {AREA workshop @ ACM Multimedia},
  year = {2008},
  month = {October},
  keywords = {}