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19 May 2015 Context and quality estimation in video for enhanced event detection
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Numerous practical applications for automated event recognition in video rely on analysis of the objects and their associated motion, i.e., the kinematics of the scene. The ability to recognize events in practice depends on accurate tracking objects of interest in the video data and accurate recognition of changes relative to the background. Numerous factors can degrade the performance of automated algorithms. Our object detection and tracking algorithms estimate the object position and attributes within the context of a dynamic assessment of video quality, to provide more reliable event recognition under challenging conditions. We present an approach to robustly modeling the image quality which informs tuning parameters to use for a given video stream. The video quality model rests on a suite of image metrics computed in real-time from the video. We will describe the formulation of the image quality model. Results from a recent experiment will quantify the empirical performance for recognition of events of interest.
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John M. Irvine and Richard J. Wood "Context and quality estimation in video for enhanced event detection", Proc. SPIE 9460, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XII, 94600L (19 May 2015);

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