Vision-Based Potential Pedestrian Risk Analysis on Unsignalized Crosswalk Using Data Mining Techniques

Though the technological advancement of smart city infrastructure has significantly improved urban pedestrians’ health and safety, there remains a large number of road traffic accident victims, making it a pressing current transportation concern. In particular, unsignalized crosswalks present a major threat to pedestrians, but we lack dense behavioral data to understand the risks they face. In this study, we propose a new model for potential pedestrian risky event (PPRE) analysis, using video footage gathered by road security cameras already installed at such crossings. Our system automatically detects vehicles and pedestrians, calculates trajectories, and extracts frame-level behavioral features. We use k-means clustering and decision tree algorithms to classify these events into six clusters, then visualize and interpret these clusters to show how they may or may not contribute to pedestrian risk at these crosswalks. We confirmed the feasibility of the model by applying it to video footage from unsignalized crosswalks in Osan city, South Korea

Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System

Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users’ risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea

Stress Detection and Classification of Laying Hens by Sound Analysis

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory