Driver's fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles (such as buses and heavy trucks) due to prolonged driving periods and boredom in working conditions. In this paper, we propose a vision-based fatigue detection system for bus driver monitoring, which is easy and flexible for deployment in buses and large vehicles. The system consists of modules of head-shoulder detection, face detection, eye detection, eye openness estimation, fusion, drowsiness measure percentage of eyelid closure (PERCLOS) estimation, and fatigue level classification. The core innovative techniques are as follows: 1) an approach to estimate the continuous level of eye openness based on spectral regression; and 2) a fusion algorithm to estimate the eye state based on adaptive integration on the multimodel detections of both eyes. A robust measure of PERCLOS on the continuous level of eye openness is defined, and the driver states are classified on it. In experiments, systematic evaluations and analysis of proposed algorithms, as well as comparison with ground truth on PERCLOS measurements, are performed. The experimental results show the advantages of the system on accuracy and robustness for the challenging situations when a camera of an oblique viewing angle to the driver's face is used for driving state monitoring.

In this paper, we explore a new way to provide context-aware assistance for indoor navigation using a wearable vision system. We investigate how to represent the cognitive knowledge of wayfinding based on first-person-view videos in real-time and how to provide context-aware navigation instructions in a human-like manner. Inspired by the human cognitive process of wayfinding, we propose a novel cognitive model that represents visual concepts as a hierarchical structure. It facilitates efficient and robust localization based on cognitive visual concepts. Next, we design a prototype system that provides intelligent context-aware assistance based on the cognitive indoor navigation knowledge model. We conducted field tests and evaluated the system's efficacy by benchmarking it against traditional 2D maps and human guidance. The results show that context-awareness built on cognitive visual perception enables the system to emulate the efficacy of a human guide, leading to positive user experience.

Recognizing users' engagement state and intentions is a pressing task for computational agents to facilitate fluid conversations in situated interactions. We investigate how to quantitatively evaluate high-level user engagement and intentions based on low-level visual cues, and how to design engagement-aware behaviors for the conversational agents to behave in a sociable manner. Drawing on machine learning techniques, we propose two computational models to quantify users' attention saliency and engagement intentions. Their performances are validated by a close match between the predicted values and the ground truth annotation data. Next, we design a novel engagement-aware behavior model for the agent to adjust its direction of attention and manage the conversational floor based on the estimated users' engagement. In a user study, we evaluated the agent's behaviors in a multiparty dialog scenario. The results show that the agent's engagement-aware behaviors significantly improved the effectiveness of communication and positively affected users' experience.

In this paper, a novel vision system is proposed to estimate attention of people from rich visual clues for social robot to perform natural interactions with multiple participants in public environments. The vision detection and recognition modules include multi-person detection and tracking, upper-body pose recognition, face and gaze detection, lip motion analysis for speaking recognition, and facial expression recognition. A computational approach is proposed to generate a quantitative estimation of human attention. The vision system is implemented on a robotic receptionist "EVE" and encouraging results have been obtained.