Abstract
Detecting pedestrians efficiently and accurately is a fundamental step for many computer vision applications, such as smart cars and robotics. In this paper, we introduce a pedestrian detection system to extract human objectives using an on-board monocular camera. First of all, we use an experiment to demonstrate that the orientation information is critical in human detection. Secondly, the local binary patterns-based feature, oriented LBP (OLBP), is discussed. The OLBP feature integrates pixel intensity difference with texture orientation information to capture salient object features. Thirdly, a set of edge orientation histogram (EOH) and OLBP-based intrablock and interblock features is presented to describe cell-level and block-level structure information. These multilevel features capture larger-scale structure information which is more informative for pedestrian localization. Experiments on the Institut national de recherche en informatique et en automatique (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 also performs at a faster speed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Circuits and Systems for Video Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
