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Handling Occlusions with Franken-classifiers

Markus Mathias, Rodrigo Benenson, Radu Timofte and Luc Van Gool
International Conference on Computer Vision (ICCV 2013)
Australia, December 2013

Abstract

Detecting partially occluded pedestrians is challenging. A common practice to maximize detection quality is to train a set of occlusion-specific classifiers, each for a certain amount and type of occlusion. Since training classifiers is expensive, only a handful are typically trained. We show that by using many occlusion-specific classifiers, we outperform previous approaches on three pedestrian datasets; INRIA, ETH, and Caltech USA. We present a new approach to train such classifiers. By reusing computations among different training stages, 16 occlusion-specific classifiers can be trained at only one tenth the cost of one full training. We show that also test time cost grows sub-linearly.


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@InProceedings{eth_biwi_01048,
  author = {Markus Mathias and Rodrigo Benenson and Radu Timofte and Luc Van Gool},
  title = {Handling Occlusions with Franken-classifiers},
  booktitle = {International Conference on Computer Vision (ICCV 2013)},
  year = {2013},
  month = {December},
  keywords = {}
}