Sciweavers

Share
ECCV
2010
Springer

Automatic Learning of Background Semantics in Generic Surveilled Scenes

10 years 7 months ago
Automatic Learning of Background Semantics in Generic Surveilled Scenes
Advanced surveillance systems for behavior recognition in outdoor traffic scenes depend strongly on the particular configuration of the scenario. Scene-independent trajectory analysis techniques sta- tistically infer semantics in locations where motion occurs, and such inferences are typically limited to abnormality. Thus, it is interesting to design contributions that automatically categorize more specific semantic regions. State-of-the-art approaches for unsupervised scene labeling exploit trajectory data to segment areas like sources, sinks, or waiting zones. Our method, in addition, incorporates scene-independent knowledge to assign more meaningful labels like crosswalks, sidewalks, or parking spaces. First, a spatiotemporal scene model is obtained from trajectory analysis. Subsequently, a so-called GI-MRF inference process reinforces spatial coherence, and incorporates taxonomy-guided smoothness constraints. Our method achieves automatic and effective labeling of conceptual regio...
Carles Fernández, Jordi Gonzàlez, Xavier Roca
Added 03 Sep 2010
Updated 16 Dec 2010
Type Conference
Year 2010
Where ECCV
Authors Carles Fernández, Jordi Gonzàlez, Xavier Roca
Comments (0)
books