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Pedestrian candidates generation using monocular cues

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3 Author(s)
Diego Cheda ; Computer Vision Center and Computer Science Department, Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Barcelona, Spain ; Daniel Ponsa ; Antonio M. López

Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached.

Published in:

Intelligent Vehicles Symposium (IV), 2012 IEEE

Date of Conference:

3-7 June 2012