Browsing by Author "Atev, Stefan"
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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 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.Item 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.