Abstract
This paper presents the design and application of novel multisensory testbeds for collection, synchronization, archival, and analysis of multimodal data for health monitoring of transportation infrastructures. The framework for data capture from vision and seismic sensors is described, and the important issue of synchronization between these modalities is addressed. Computer-vision algorithms are used to detect and track vehicles and extract their properties. It is noted that the video and seismic sensors in the testbed supply complementary information about passing vehicles. Data fusion between features obtained from these modalities is used to perform vehicle classification. Experimental results of vehicle detection, tracking, and classification obtained with these testbeds are described
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.
