Browsing by Author "Bird, Nathaniel"

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    Finding What the Driver Does
    (2005-05-01) Veeraraghavan, Harini; Atev, Stefan; Bird, Nathaniel; Schrater, Paul; Papanikolopoulos, Nikolaos P
    Most 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.
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    Recognition of Human Activity in Metro Transit Spaces
    (2004-06-01) Gasser, Gillaume; Bird, Nathaniel; Papanikolopoulos, Nikolaos P
    In this report, we introduce a vision-based system to monitor for suspicious human activities at a bus stop. The system currently examines behavior for drug dealing activities which is characterized by individuals loitering around the bus stop for a very long time with no intention of using the bus. To accomplish this goal, the system must measure how long individuals loiter around the bus stop. To facilitate this, the system must track individuals from the video feed, identify them, and keep a record of how long they spend at the bus stop. The system is broken into three distinct portions: background subtraction, object tracking, and human recognition. The background subtraction and object tracking modules use off-the-shelf algorithms and are shown to work well following people as they walk around a bus stop. The human recognition module segments the image of an individual into three portions corresponding to the head, torso, and legs. Using the median color of each of these regions, two people can be quickly compared to see if they are the same person.