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Browsing by Author "Drenner, Andrew"

Now showing 1 - 2 of 2
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    Autonomous Docking for an eROSI Robot Based on a Vision System with Points Clustering
    (2007-01-26) Min Jeong, Hyeun; Drenner, Andrew; Papanikolopoulos, Nikos
    This paper presents an autonomous docking system based on visual cues on a docking station. Autonomous docking is essential for large scale robotic teams to be delivered by larger robots, recovered, recharged, and redeployed for continuous operation. Using a computer vision based approach, we identify cues to line up for docking by extracting corner pixels and combining this information with color information. Potential target points are extracted and clustered using Euclidean distance in the image plane. Using these clusters of points the appropriate motion behavior is selected to reposition the robot into the desired position and orientation. This paper will present examples of this implementation using an eROSI robot which uses only vision to navigate.
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    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.