Detecting Work Zones in SHRP 2 NDS Videos Using Deep Learning Based Computer Vision
This work addresses the need for automated video analytics to support transportation safety research by handling massive datasets, though it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackled the problem of automatically detecting work zones in a large-scale naturalistic driving video dataset (SHRP 2 NDS) using deep learning-based computer vision, resulting in the development of the SNVA software application that integrates extracted information into a roadway database to enable efficient analysis of the 2PB dataset.
Naturalistic driving studies seek to perform the observations of human driver behavior in the variety of environmental conditions necessary to analyze, understand and predict that behavior using statistical and physical models. The second Strategic Highway Research Program (SHRP 2) funds a number of transportation safety-related projects including its primary effort, the Naturalistic Driving Study (NDS), and an effort supplementary to the NDS, the Roadway Information Database (RID). This work seeks to expand the range of answerable research questions that researchers might pose to the NDS and RID databases. Specifically, we present the SHRP 2 NDS Video Analytics (SNVA) software application, which extracts information from NDS-instrumented vehicles' forward-facing camera footage and efficiently integrates that information into the RID, tying the video content to geolocations and other trip attributes. Of particular interest to researchers and other stakeholders is the integration of work zone, traffic signal state and weather information. The version of SNVA introduced in this paper focuses on work zone detection, the highest priority. The ability to automate the discovery and cataloging of this information, and to do so quickly, is especially important given the two petabyte (2PB) size of the NDS video data set.