Abstract
Pedestrian detection in complex scenes has always been a research difficulty in computer vision. The performance of current methods was seriously degraded when the pedestrian is occluded or the size of pedestrians are too small, etc. In this paper, we propose a novel approach based on location model for the detection of multiple pedestrians, which aims to improve the efficiency of algorithms in complex scenarios. In the model, a fully convolutional neural networks for the classification of pedestrian and non-pedestrian are trained to learn pedestrian features first. Then trained model are used to search for pedestrian regions, and the regions where pedestrian may be present will be activated and marked with boxes. Finally, we fine tune the boxes to overlap them with the ground truth more precisely. Compared with the current methods on two pedestrian datasets, experimental results demonstrate the comparable performance of our approach in term of miss rates.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IOP Conference Series: Materials Science and Engineering
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.