Integration of visual and depth information for vehicle detection

Alexandros Makris 1 Mathias Perrollaz 1 Igor Paromtchik 1 Christian Laugier 1
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : In this work an object class recognition method is presented. The method uses local image features and follows the part based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given scale. To train the system for an object class only a database of annotated with bounding boxes images is required, thus automatizing the extension of the system to different object classes. We apply our method to the problem of detecting vehicles from a moving platform. The experiments with a dataset of stereo images in an urban environment show a significant improvement in performance when using both information modalities.
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[Research Report] RR-7703, INRIA. 2011
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Alexandros Makris, Mathias Perrollaz, Igor Paromtchik, Christian Laugier. Integration of visual and depth information for vehicle detection. [Research Report] RR-7703, INRIA. 2011. 〈inria-00613316〉

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