Browsing by Author "Veeraraghavan, Harini"
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Item Detecting Driver Fatigue Through the Use of Advanced Face Monitoring Techniques(2001-09-01) Veeraraghavan, Harini; Papanikolopoulos, Nikolaos PDriver fatigue is an important factor in many vehicular accidents. Reducing the number of fatigue-related accidents would save society a significant amount financially, in addition to reducing personal suffering. The researchers developed a driver fatigue monitoring system that uses a camera (or cameras) to detect indications of driver fatigue. The mechanism detects and tracks the eyes of the driver based on human skin color properties, along with templates that monitor how long the eyes are open or closed. Tests of the approach were run on 20 human subjects in a simulated environment (the driving simulator at the Human Factors Research Laboratory) in order to find its potential and its limitations. This report describes the findings from these experiments.Item Development of a Tracking-based Monitoring and Data Collection System(2005-10-01) Veeraraghavan, Harini; Atev, Stefan; Masoud, Osama; Miller, Grant; Papanikolopoulos, Nikolaos PThis report outlines a series of vision-based algorithms for data collection at traffic intersections. We have purposed an algorithm for obtaining sound spatial resolution, minimizing occlusions through an optimization-based camera-placement algorithm. A camera calibration algorithm, along with the camera calibration guided user interface tool, is introduced. Finally, a computationally simple data collection system using a multiple cue-based tracker is also presented. Extensive experimental analysis of the system was performed using three different traffic intersections. This report also presents solutions to the problem of reliable target detection and tracking in unconstrained outdoor environments as they pertain to vision-based data collection at traffic intersections.Item Finding What the Driver Does(2005-05-01) Veeraraghavan, Harini; Atev, Stefan; Bird, Nathaniel; Schrater, Paul; Papanikolopoulos, Nikolaos PMost research depends on detection of driver alertness through monitoring the eyes, face, head or facial expression. This research presents methods for recognizing and summarizing the activities of drivers using the appearance of the driver's position, and changes in position, as fundamental cues, based on the assumption that periods of safe driving are periods of limited motion in the driver's body. The system uses a side-mounted camera and utilizes silhouettes obtained from skin color segmentation for detecting activities. The unsupervised method uses agglomerative clustering to represent driver activity throughout a sequence, while the supervised learning method uses a Bayesian eigen image classifier to distinguish between activities. The results validate the advantages of using driver appearance obtained from skin color segmentation for classification and clustering purposes. Advantages include increased robustness to illumination variations and elimination of the need for tracking and pose determination.Item Managing Suburban Intersections through Sensing(2002-12-01) Veeraraghavan, Harini; Masoud, Osama; Papanikolopoulos, Nikolaos PIncreased urban sprawl and increased vehicular traffic have resulted in an increased number of traffic fatalities, the majority of which occur near intersections. According to the National Highway Safety Administration, one out of eight fatalities occurring at intersections is a pedestrian. An intelligent, real-time system capable of predicting situations leading to accidents or near misses will be very useful to improve the safety of pedestrians as well as vehicles. This project investigates the prediction of such situations using current traffic conditions and computer vision techniques. An intelligent system may gather and analyze such data in a scene (e.g., vehicle and pedestrian positions, trajectories, velocities, etc.) and provide necessary warnings. The current work focuses on the monitoring aspect of the project. Certain solutions are proposed and issues with the current implementation are highlighted. The cost of the proposed system is low and certain operational characteristics are presented.Item Performance Evaluation of a Multi-Robot Search & Retrieval System: Experiences with MinDART(2003-02-08) Rybski, Paul E.; Veeraraghavan, Harini; Lapoint, Monica; Gini, MariaThe costs of developing mobile robot teams can be reduced if they are designed to exploit swarm techniques. In this methodology many simple homogeneous units solve complex tasks through emergent behavior. The challenge lies in selecting an appropriate control strategy for the individual units. Complexity in design costs both money and time, therefore a control strategy should be just complex enough to perform the task successfully in a variety of environments, relative to some performance measure. To explore the effects of control strategies and environmental factors on performance, we have conducted two sets offoraging experiments using real robots (the {em Minnesota Distributed Autonomous Robotic Team}). The first set of experiments tested the efficacy of localization capabilities, in addition to the effects of team size and target distribution. The second set tested the efficacyof communication. We found that more complex control strategies do not necessarily improve task completion times, however they can reduce variance in performance measures. This can be valuable information for designers who need to assess the potential costs and benefits of increased complexity in design.Item Portable Traffic Data Processor(University of Minnesota Center for Transportation Studies, 2008-09) Papanikolopoulos, Nikoloas; Veeraraghavan, HariniAutomatic extraction of events from video sequences has important applications in a variety of Intelligent Transportation Systems (ITS) problems including scene monitoring, traffic data collection, intersection monitoring, etc. When deploying a system that recognizes events automatically from video sequences, two important things to consider are the real-time analysis of the video sequences and fast learning times required for learning the different classes of events in a scene. A related requirement which is often ignored is the limited reliance of the learning system on the user provided knowledge. In this work, we present an innovative technique for detecting the different events in video sequences through a semi-supervised learning method.