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Despite recent advances in vehicle safety technologies, road trafficaccidents still pose a severe threat to human lives and have become a leadingcause of premature deaths. In particular, crosswalks present a major threat topedestrians, but we lack dense behavioral data to investigate the risks theyface. Therefore, we propose a comprehensive analytical model for pedestrianpotential risk using video footage gathered by road security cameras deployedat such crossings. The proposed system automatically detects vehicles andpedestrians, calculates trajectories by frames, and extracts behavioralfeatures affecting the likelihood of potentially dangerous scenes between theseobjects. Finally, we design a data cube model by using the large amount of theextracted features accumulated in a data warehouse to perform multidimensionalanalysis for potential risk scenes with levels of abstraction, but this isbeyond the scope of this paper, and will be detailed in a future study. In ourexperiment, we focused on extracting the various behavioral features frommultiple crosswalks, and visualizing and interpreting their behaviors andrelationships among them by camera location to show how they may or may notcontribute to potential risk. We validated feasibility and applicability byapplying it in multiple crosswalks in Osan city, Korea.

Vision based Pedestrian Potential Risk Analysis based on Automated Behavior Feature Extraction for Smart and Safe City