Video and Seismic Sensor-Based Structural Health Monitoring: Framework, Algorithms, and Implementation

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