Browsing by Author "Papanikolopoulos, Nikolaos P."

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Automatic Detection of Driver Fatigue - Phase III
(Minnesota Department of Transportation, 1999-06) Kaur, Sarbjit Sing; Eriksson, Martin; Papanikolopoulos, Nikolaos P.
Traffic centers gather information from traffic sensors at regular intervals, but storing the data for future analysis becomes an issue. This report details work to improve the speed and effectiveness of traffic databases. In this project phase, researchers redesigned the data model based on the previous phase's data model and decreased the storage requirements by one-third. Researchers developed a web-based Graphical User Interface (GUI) for users to specify the query of interest; the outcome of the performance tuning gave users reasonable response time. The beneficiaries of this effective database would include the driving public, traffic engineers, and researchers, who are generally not familiar with the query language used in the database management system. This report summarizes the detailed reference, such as benchmark query, sample data, table schema, conversion code, and other information.
Detecting Driver Fatigue Through the Use of Advanced Face Monitoring Techniques
(Center for Transportation Studies, University of Minnesota, 2001-09-01) Veeraraghavan, Harini; Papanikolopoulos, Nikolaos P.
Driver 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.
Freeway Network Traffic Detection and Monitoring Incidents
(Minnesota Department of Transportation, 2007-10) Joshi, Ajay J.; Atev, Stefan; Fehr, Duc; Drenner, Andrew; Bodor, Robert; Masoud, Osama; Papanikolopoulos, Nikolaos P.
We propose methods to distinguish between moving cast shadows and moving foreground objects in video sequences. Shadow detection is an important part of any surveillance system as it makes object shape recovery possible, as well as improves accuracy of other statistics collection systems. As most such systems assume video frames without shadows, shadows must be dealt with beforehand. We propose a multi-level shadow identification scheme that is generally applicable without restrictions on the number of light sources, illumination conditions, surface orientations, and object sizes. In the first level, we use a background segmentation technique to identify foreground regions that include moving shadows. In the second step, pixel-based decisions are made by comparing the current frame with the background model to distinguish between shadows and actual foreground. In the third step, this result is improved using blob-level reasoning that works on geometric constraints of identified shadow and foreground blobs. Results on various sequences under different illumination conditions show the success of the proposed approach. Second, we propose methods for physical placement of cameras in a site so as to make the most of the number of cameras available.
Multi-Camera Monitoring of Human Activities at Critical Transportation Infrastructure Sites
(University of Minnesota Center for Transportation Studies, 2008-06) Ribnick, Evan; Joshi, Ajay J.; Papanikolopoulos, Nikolaos P.
The goal of this work is to provide a system which can aid in monitoring crowded urban environments, which often contain tight groups of people. In this report, we consider the problem of counting the number of people in the scene and also tracking them reliably. We propose a novel method for detecting and estimating the count of people in groups, dense or otherwise, as well as tracking them. Using prior knowledge obtained from the scene and accurate camera calibration, the system learns the parameters required for estimation. This information can then be used to estimate the count of people in the scene, in real time. Groups are tracked in the same manner as individuals, using Kalman filtering techniques. Favorable results are shown for groups of various sizes moving in an unconstrained fashion.
Pedestrian Control at Intersections (Phase I)
(Minnesota Department of Transportation, 1996-10) Papanikolopoulos, Nikolaos P.; Masoud, Osama; Richards, Charles A.
This report describes a real-time system for tracking pedestrians in sequences of grayscale images acquired by a stationary camera. The system outputs the spatio-temporal coordinates of each pedestrian during the period when the pedestrian is visible. Implemented on a Datacube MaxVideo 20 equipped with a Datacube Max 860, the system achieved a peak performance of over 30 framers per second. Experimental results based on indoor and outdoor scenes have shown that the system is robust under many difficult traffic situations. The system uses the "figure/ground" framework to accomplish the goal of pedestrian detection. The detection phase outputs the tracked blobs (regions), which in turn pass to the final level, the pedestrian level. The pedestrian level deals with pedestrian models and depends on the tracked blobs as the only source of input. By doing this, researchers avoid trying to infer information about pedestrians directly from raw images, a process that is highly sensitive to noise. The pedestrian level makes use of Kalman filtering to predict and estimate pedestrian attributes. The filtered attributes constitute the output of this level, which is the output of the system. This system was designed to be robust to high levels of noise and particularly to deal with difficult situations, such as partial or full occlusions of pedestrians. The report compares vision sensors with other types of possible sensors for the pedestrian control task and evaluates the use of active deformable models as an effective pedestrian tracking module.
Pedestrian Control at Intersections - Phase III
(Minnesota Department of Transportation, 1998-04) Masoud, Osama; Papanikolopoulos, Nikolaos P.
This report presents a real-time system for pedestrian tracking in sequences of grayscale images acquired by a stationary CCD (charged-coupled devices) camera. The research objective involves integrating this system with a traffic control application, such as a pedestrian control scheme at intersections. The system outputs the spatiotemporal coordinates of each pedestrian during the period the pedestrian remains in the scene. The system processes at three levels: raw images, blobs, and pedestrians. It models blob tracking as a graph optimization problem and pedestrians as rectangular patches with a certain dynamic behavior. Kalman filtering is used to estimate pedestrian parameters. The system was implemented on a Datacube MaxVideo 20 equipped with a Datacube Max860 and on a Pentium-based PC. The system achieved a peak performance of more than 20 frames per second. Experimental results based on indoor and outdoor scenes demonstrated the system's robustness under many difficult situations such as partial or full occlusions of pedestrians.
Pedestrian Control at Intersections: Phase IV
(Minnesota Department of Transportation, 2000-02-01) Masoud, Osama; Papanikolopoulos, Nikolaos P.
This report represents a real-time system for pedestrian tracking in sequences of greyscale images acquired by a stationary camera. Researchers also developed techniques for recognizing pedestrians' actions, such as running and walking, and integrated the system with a pedestrian control scheme at intersections. The proposed approach can be used to detect and track humans in a variety of applications. Furthermore, the proposed schemes also can be employed for the detection of several diverse traffic objects of interest, such as vehicles or bicycles. The system outputs the spatio-temporal coordinates of each pedestrian during the period that the pedestrian is in the scene. The system processes at three levels: raw images, blobs and pedestrians. Experimental results based on indoor and outdoor scenes demonstrated the system's robustness under many difficult situations such as partial or full occlusions of pedestrians. In particular, this report contains the results from a field test of the system conducted in November 1999. Keywords: pedestrian detection and tracking, action recognition, pedestrian control at intersections
Practical Methods for Analyzing Pedestrian and Bicycle Use of a Transportation Facility
(Minnesota Department of Transportation Office of Research Services, 2010-02) Somasundaram, Guruprasad; Morellas, Vassilios; Papanikolopoulos, Nikolaos P.
The objective of the project is to analyze existing technologies used for the process of generating counts of bicycles and pedestrians in transportation facilities such as walk and bicycle bridges, urban bicycle routes, bicycle trails etc. The advantages and disadvantages of each existing technology which is being applied to counting has been analyzed and some commercially available products were listed. A technical description of different methods that were considered for vision based object recognition is also mentioned along with the reasons as to why such methods were overlooked for our problem. Support Vector Machines were used for classification based on a vocabulary of features built using interest point detectors. After finalizing the software and hardware, five sites were picked for filming and about 10 hours of video was acquired in all. A portion of the video data was used for training and the remainder was used for testing the algorithm’s accuracy. Results of counts are provided and an interpretation of these results is provided in this report. Upon detailed analysis the reasons for false counts and undercounting in some cases have been identified and current work concerns dealing with these issues. Changes are being made to the system to improve the accuracy with the current level of training and make the system available for practitioners to perform counting.
Real-Time Collision Warning and Avoidance at Intersections
(Minnesota Department of Transportation, 2004-11-01) Atev, Stefan; Masoud, Osama; Janardan, Ravi; Papanikolopoulos, Nikolaos P.
Monitoring traffic intersections in real-time as well as predicting possible collisions is an important first step towards building an early collision warning system. We present the general vision methods used in a system addressing this problem and describe the practical adaptations necessary to achieve real-time performance. A novel method for three dimensional vehicle size estimation is presented. We also describe a method for target localization in real-world coordinates, which allows for sequential incorporation of measurements from multiple cameras into a single target's state vector. Additionally, a fast implementation of a false-positive reduction method for the foreground pixel masks is developed. Finally, a low-overhead collision prediction algorithm using the time-as-axis paradigm is presented. The proposed system was able to perform in real-time on videos of quarter-VGA ($320\times240$) resolution. The errors in target position and dimension estimates in a test video sequence are quantified.
Using CCD Cameras for Obstacle Avoidance and Detection of Pedestrians
(Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 1997-07) Papanikolopoulos, Nikolaos P.
The complexity and congestion of current transportation systems often produce traffic situations that jeopardize the safety of the people involved. These situations vary from maintaining a safe distance behind a leading vehicle to safely allowing a pedestrian to cross a busy street. Environmental sensing plays a critical role in virtually all of these situations. Of the sensors available, vision sensors provide information that is richer and more complete than other sensors, making them a logical choice for a multisensor transportation system. In this report we propose robust detection and tracking techniques for intelligent vehicle-highway applications where computer vision plays a crucial role. In particular, we demonstrate that the Controlled Active Vision framework [15] can be utilized to provide a visual tracking modality to a traffic advisory system in order to increase the overall safety margin in a variety of common traffic situations. We have selected two application examples, vehicle tracking and pedestrian tracking, to demonstrate that the framework can provide precisely the type of information required to effectively manage the given traffic situation.

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Browsing by Author "Papanikolopoulos, Nikolaos P."