TY  - CONF
AU  - Carles Fernandez
AU  - Jordi Gonzalez
AU  - Xavier Roca
A2  - ECCV
PY  - 2010//
TI  - Automatic Learning of Background Semantics in Generic Surveilled Scenes
T2  - LNCS
BT  - 11th European Conference on Computer Vision
SP  - 678–692
VL  - 6313
IS  - II
PB  - Springer Berlin Heidelberg
N2  - Advanced surveillance systems for behavior recognition in outdoor traffic scenes depend strongly on the particular configuration of the scenario. Scene-independent trajectory analysis techniques statistically 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 regions in urban scenarios, and is robust to tracking errors. Experimental validation on 5 surveillance databases has been conducted to assess the generality and accuracy of the segmentations. The resulting scene models are used for model-based behavior analysis.
SN  - 0302-9743
SN  - 978-3-642-15551-2
UR  - http://dx.doi.org/10.1007/978-3-642-15552-9_49
N1  - ISE
ID  - Carles Fernandez2010
ER  -