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Tracker Trees for Unusual Event Detection

Fabian Nater, Helmut Grabner, Tobias Jaeggli and Luc Van Gool
IEEE Int. Workshop on Visual Surveillance
October 2009

Abstract

We present an approach for unusual event detection, based on a tree of trackers. At lower levels, the trackers are trained on broad classes of targets. At higher levels, they aim at more specific targets. For instance, at the root, a general blob tracker could operate which may track any object. The next level could already use information about human appearance to better track people. A further level could go after specific types of actions like walking, running, or sitting. Yet another level up, several walking trackers can be tuned for the gait of a particular person each. Thus, at each layer, one or more families of more specific trackers are available. As long as the target behaves according to expectations, a member of a higher up such family will be better tuned to the data than its parent tracker at a lower level. Typically, a better informed tracker performs more robustly. But in cases where unusual events occur and the normal assumptions about the world no longer hold, they loose their liability. In such cases, a less informed tracker, not relying on what has now become false information, has a good chance of performing better. Such performance inversion signals an unusual event. Inversions between levels higher up represent deviations that are semantically more subtle than inversions lower down: for instance an unknown intruder entering a house rather than seeing a non-human target.


Link to publisher's page
@InProceedings{eth_biwi_00669,
  author = {Fabian Nater and Helmut Grabner and Tobias Jaeggli and Luc Van Gool},
  title = {Tracker Trees for Unusual Event Detection},
  booktitle = {IEEE Int. Workshop on Visual Surveillance},
  year = {2009},
  month = {October},
  keywords = {}
}