Abstract
This paper describes a design of fast recognition of road information based on mobile terminal. Firstly, based on the HOG algorithm, we study and verify the effects of different parameters on the performance of the algorithm. Secondly, we test 800 images randomly selected from the INRIA pedestrian dataset to obtain the optimal parameters for the mobile terminal and the proportion of video resolution and detection window. Then, under the same test conditions, the time overheads of the SVMLight and the LibSVM are recorded and SVMLight training time is significantly less than LibSVM. Thirdly, we design and implement a real-time road information recognition and warning system on the Windows platform and Android platform. Features include real-time pedestrians detection, voice warning, and road signs recognition. When the vehicle speed is less than 30 km/h, the video resolution is less than 720 × 576 and the detection window/image ratio is less than 1 : 50; the system can guarantee low delay and high recognition rate (97.2%).
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.
