Abstract
The existing real-time pedestrian detection method often loses part of the detection accuracy. For this reason, we proposed a real-time pedestrian detection algorithm based on tiny-yolov3. The proposed method uses K-means clustering on our training set to find the best priors. We improved the network structure of tiny-yolov3 to make it more accurate in pedestrian detection. From the experimental results, the proposed method has higher detection accuracy under the premise of satisfying real-time performance.
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.
