TR2005-058

Shadow Flow: A Recursive Method to Learn Moving Cast Shadows


    •  Porikli, F.; Thornton, J., "Shadow Flow: A Recursive Method to Learn Moving Cast Shadows", IEEE International Conference on Computer Vision (ICCV), ISSN: 1550-5499, October 2005, vol. 1, pp. 891-898.
      BibTeX Download PDF
      • @inproceedings{Porikli2005oct,
      • author = {Porikli, F. and Thornton, J.},
      • title = {Shadow Flow: A Recursive Method to Learn Moving Cast Shadows},
      • booktitle = {IEEE International Conference on Computer Vision (ICCV)},
      • year = 2005,
      • volume = 1,
      • pages = {891--898},
      • month = oct,
      • issn = {1550-5499},
      • url = {http://www.merl.com/publications/TR2005-058}
      • }
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  • Research Area:

    Computer Vision


TR Image
Figure 11: Sample detection results without any filtering (red: shadow, green: foreground).

We present a novel algorithm to detect and remove cast shadows in a video sequence by taking advantage of the statistical prevalence of the shadowed regions over the object regions. We model shadows using multivariate Gaussians. We apply a weak classifier as a pre-filter. We project shadow models into a quantized color space to update a shadow flow function. We use shadow flow, background models, and current frame to determine the shadow and object regions. This method has several advantages: It does not require a color space transformation. We pose the problem in the RGB color space, and we carry out the same analysis in other Cartesian spaces as well. It is data-driven and adapts to the changing shadow conditions. In other words, accuracy of our method is not limited by the present threshold values. Furthermore, it does not assume any 3D models for the target objects or tracking of the cast shadows between frames. Our results show that the detection performance is superior than the benchmark method.