ICIP_UKF_VideoNetwork - OPTIMAL SENSOR SELECTION FOR...

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1 OPTIMAL SENSOR SELECTION FOR VIDEO-BASED TARGET TRACKING IN A WIRELESS SENSOR NETWORK ICIP 2004 Presented in class by Arslan Basharat (arslan@cs.ucf.edu) Goal z Target tracking in a network of multiple video cameras (sensors) z Choose the sensors such that the information utility is maximized z Average energy used is constrained
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2 Approach z Use multiple video cameras z Overlapping / Non-overlapping Cameras z Cameras are calibrated z Active cameras detect target z Tracker estimates position in 3D world z Use UKF to obtain confidence in prediction z Confidence measure as sensor utility measure Approach z Information Utility is maximized z Set of sensor that maximize the sum information utility are enabled
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3 Main Steps z Camera Calibration z Unscented Kalman Filter (UKF) z Sensor utility measure z Maximize Information Utility Outline z Camera Calibration z Filtering Stage z Bayesian Formulation z Kalman Filter z Extended Kalman Filter z Unscented Transformation z Unscented Kalman Filter z Sensor Utility Measure z Information utility maximization z Results
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4 Camera Calibration z Projective camera model Pr i = A i [R i |t i ] z Intrinsic parameters z A Matrix (f, aspect ratio, principal point and skew) z Extrinsic parameters z R rotation Matrix z t translation vector Useful Filters for Tracking z Bayesian Approach z z Kalman Filter z Approximation of Bayesian Model z z Particle Filter z Monte-Carlo Approach z Joint Probabilistic Data Association Filter z Multiple Hypothesis Tracker
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This note was uploaded on 06/13/2011 for the course CAP 6412 taught by Professor Staff during the Spring '08 term at University of Central Florida.

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ICIP_UKF_VideoNetwork - OPTIMAL SENSOR SELECTION FOR...

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