Abstract
In this work we aim to develop an onboard monocular pedestrian detection system by employing a classifier based on multi-level features. Different from most state-of-the-art detectors, our system employs a set of multi-level features to ensure high detection rate. More specifically, a set of EOH and OLBP based multi-level features is used to describe cell-level and block-level structure information. Multi-level features capture larger scale structure information which is more informative for pedestrian localization. Experiments on the INRIA dataset and the Caltech pedestrian detection benchmark demonstrate that the new pedestrian detection system is not only comparable to the existing pedestrian detectors but it also performs at a faster speed.
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