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Risk-sensitive filters for recursive estimation of motion from images

DSpace at IIT Bombay

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Title Risk-sensitive filters for recursive estimation of motion from images
 
Creator JAYAKUMAR, M
BANAVAR, RN
 
Subject integral performance index
stochastic-systems
risk-sensitive estimation
motion parameters
vision
 
Description In this paper, an Extended Risk-Sensitive Filter (ERSF) is used to estimate the motion parameters of an object recursively from a sequence of monocular images. The effect of varying the risk factor a on the estimation error is examined. The performance of the filter is compared with the Extended Kalman Filter (EKF) and the theoretical Cramer-Rao lower bound. When the risk factor theta and the uncertainty in the measurement noise are large, the initial estimation error of the ERSF is less than that of the corresponding EKF. The ERSF is also found to converge to the steady state value of the error faster that the EKF. In situations when the uncertainty in the initial estimate is large and the EKF diverges, the ERSF converges with small errors. In confirmation with the theory, as theta tends to zero, the behavior of the ERSF is the same as that of the EKF.
 
Publisher IEEE COMPUTER SOC
 
Date 2011-07-31T14:56:15Z
2011-12-26T12:53:03Z
2011-12-27T05:40:09Z
2011-07-31T14:56:15Z
2011-12-26T12:53:03Z
2011-12-27T05:40:09Z
1998
 
Type Article
 
Identifier IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 20(6), 659-666
0162-8828
http://dx.doi.org/10.1109/34.683783
http://dspace.library.iitb.ac.in/xmlui/handle/10054/8152
http://hdl.handle.net/10054/8152
 
Language en