CS Events

Qualifying Exam Human Behavior Detection for Cyber-Physical Systems
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Friday, December 22, 2023, 09:00am - 10:30am

Speaker: Yuequn Zhang

Location : CoRE 301

Committee: 

Associate Professor Desheng Zhang

Assistant Professor Hao Wang

Distinguished Professor Dimitris Metaxas

Assistant Professor Aaron Bernstein

Event Type: Qualifying Exam

Abstract: As the integration of cyber-physical systems into everyday life advances, understanding human behavior becomes crucial for ensuring seamless, safe, and efficient interaction between humans and technology. One such application is in the realm of micro-mobility, a mode of transportation that has gained immense popularity among commuters for its convenience, low cost, and eco-friendly nature. However, despite these advantages, micro-mobility presents unique challenges, particularly in terms of rider safety. Unlike traditional transportation methods such as cars and public transit, micro-mobility vehicles often lack comprehensive safety features or designs to mitigate potential hazards. The high rate of accidents in micro-mobility, often linked to distraction, affecting riders across all experience levels, underscores the need for enhanced safety measures. Therefore, understanding where riders are looking (i.e., gaze following) is essential in preventing potential accidents and enhancing road safety. In this work, we propose a novel two-stage coarse-to-fine gaze following framework utilizing video frames streamed from smartphone dual cameras. Initially, gaze vectors are estimated from riders' facial appearances using a lightweight deep network, enabling the cropping of approximate gaze target regions. The next stage of our framework involves leveraging the visual information within these estimated regions to predict areas likely to attract attention (saliency, a bottom-up mechanism). Furthermore, we acknowledge that human gaze behavior is heavily influenced by intentional directives (a top-down mechanism). We categorize riders' gaze behavior into three distinct types: forward-fixation, target pursuit, and saccade. By integrating both bottom-up and top-down mechanisms, our approach facilitates implicit calibration and refinement specific to the riding context. This methodology aims to provide a more accurate and contextually relevant understanding of rider behavior and attention, ultimately contributing to the safety and efficiency of micro-mobility systems.

: 

Contact  Associate Professor Desheng Zhang

Zoom Link: https://rutgers.zoom.us/j/95035040609?pwd=OWZJUWszSEpFb0RIYlVJWldGM0tQZz09