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A region driven and contextualized pedestrian detector

Abstract : This paper tackles the real-time pedestrian detection problem using a stationary calibrated camera. Problems frequently encountered are: a generic classifier can not be adjusted to each situation and the perspective deformations of the camera can profoundly change the appearance of a person. To avoid these drawbacks we contextualized a detector with information coming directly from the scene. Our method comprises three distinct parts. First an oracle gathers examples from the scene. Then, the scene is split in different regions and one classifier is trained for each one. Finally each detector are automatically tuned to achieve the best performances. Designed for making camera network installation procedure easier, our method is completely automatic and does not need any knowledge about the scene.
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Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Thursday, July 19, 2018 - 3:58:06 PM
Last modification on : Saturday, June 25, 2022 - 9:11:44 PM


  • HAL Id : cea-01844716, version 1



T. Chesnais, T. Chateau, N. Allezard, Y. Dhome, B. Meden, et al.. A region driven and contextualized pedestrian detector. 8th International Conference on Computer Vision Theory and Applications, VISAPP 2013, Feb 2013, Barcelona, Spain. pp.796-799. ⟨cea-01844716⟩



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