Abstract
Vision-based pedestrian detection remains a challenging task, so far. The detection performance often suffers from the various appearances of pedestrians, the illumination changes, and the possible partial occlusions. Aiming at resolving these challenges, in this paper, a new linear kernel function is proposed to effectively combine two heterogeneous features, i.e., histogram of oriented gradient and local binary pattern, which enhances the pedestrian description ability to illumination conditions and cluttered background. Then, a novel multi-view–pose part ensemble (MVPPE) detector is proposed, in order to better handle pedestrian variability, views, and partial occlusions. Experimental results in public data sets demonstrate that the proposed feature combination method significantly improves the description capabilities of pedestrian features. Compared with the existing multipart ensemble approaches, the proposed MVPPE detector boosts higher detection accuracy.
Published Version
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 Intelligent Transportation Systems
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.
