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

TL;DR

This paper presents a video-based analytical model that automatically detects and analyzes pedestrian and vehicle behaviors at crosswalks to assess potential risks, aiming to enhance smart city safety measures.

Contribution

It introduces an automated system for extracting behavioral features from video footage to evaluate pedestrian risk levels at crosswalks, with validation in real-world settings.

Findings

Behavioral features can be effectively extracted from video data.

The model visualizes relationships between behaviors and risk levels.

Feasibility demonstrated in multiple crosswalks in Korea.

Abstract

Despite recent advances in vehicle safety technologies, road traffic accidents still pose a severe threat to human lives and have become a leading cause of premature deaths. In particular, crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. Therefore, we propose a comprehensive analytical model for pedestrian potential risk using video footage gathered by road security cameras deployed at such crossings. The proposed system automatically detects vehicles and pedestrians, calculates trajectories by frames, and extracts behavioral features affecting the likelihood of potentially dangerous scenes between these objects. Finally, we design a data cube model by using the large amount of the extracted features accumulated in a data warehouse to perform multidimensional analysis for potential risk scenes with levels of abstraction, but this is beyond the scope of this paper, and will be detailed in a future study. In our experiment, we focused on extracting the various behavioral features from multiple crosswalks, and visualizing and interpreting their behaviors and relationships among them by camera location to show how they may or may not contribute to potential risk. We validated feasibility and applicability by applying it in multiple crosswalks in Osan city, Korea.