Abstract
Macrofeatures are mid-level features that jointly encode a set of low-level features in a neighborhood. We propose a macrofeature layout selection technique to improve localization performance in an object detection task. Our method employs line, triangle, and pyramid layouts, which are composed of several local blocks represented by the Histograms of Oriented Gradients (HOGs) features in a multi-scale feature pyramid. Such macrofeature layouts are integrated into a boosting framework for object detection, where the best layout is selected to build a weak classifier in a greedy manner at each iteration. The proposed algorithm is applied to pedestrian detection and implemented using GPU. Our pedestrian detection algorithm performs better in terms of detection and localization accuracy with great efficiency when compared to several state-of-the-art techniques in public datasets.
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 callDisclaimer: 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.
