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Automatic Learning of Background Semantics in Generic Surveilled Scenes

  • Carles Fernández
  • Jordi Gonzàlez
  • Xavier Roca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)

Abstract

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.

Keywords

Ground Truth Segmentation Accuracy Parking Space Smoothness Constraint Ground Truth Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Carles Fernández
    • 1
  • Jordi Gonzàlez
    • 1
  • Xavier Roca
    • 1
  1. 1.Dept. Ciències de la Computació & Computer Vision Center, Edifici O, Campus UABBarcelonaSpain

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