Unusual event detection in wide-angle video (based on moving object trajectories)
First Claim
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1. A method of monitoring moving objects in a wide-angle video, comprising the steps of:
- determining moving object trajectories;
converting the moving object trajectories to a trajectory in a perspectively corrected image domain; and
interpreting the object trajectories for detecting unusual behavior using state transition probability models of non-hidden Markov models.
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Abstract
Object images captured by a wide-angle camera are distorted due to the optical effects of the wide-angle lens. The disclosed innovations allow an automatic analysis on the corrected image distinguishing normal movement from an unusual event movement. The analysis is based on Markov Modeling on moving object trajectories and motion angles.
125 Citations
19 Claims
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1. A method of monitoring moving objects in a wide-angle video, comprising the steps of:
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determining moving object trajectories; converting the moving object trajectories to a trajectory in a perspectively corrected image domain; and interpreting the object trajectories for detecting unusual behavior using state transition probability models of non-hidden Markov models. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method of monitoring moving objects in a wide-angle video, comprising the steps of:
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determining moving object trajectories; and interpreting the object trajectories for detecting unusual behavior for an unusual event in a distorted image domain using a state transition analysis of Markov Models. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A system for detecting unusual events in a wide-angle video, comprising:
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an electronic camera that emulates PTZ function capturing wide-angle digital video images; and an image data processor that calculates and converts moving pixel blob trajectories in a non-rectilinear image domain and analyzes the blob trajectories as unusual behavior using state transition probability models applied to a data object histogram; wherein the state transition probability models are trained using prior trajectory data corresponding to regular and unusual motion trajectories of moving objects, and wherein the state transition probability models are non-hidden Markov models. - View Dependent Claims (17, 18, 19)
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Specification