TY - GEN
T1 - Multiframe distortion-tolerant correlation filtering for video sequences
AU - Kerekes, R.
AU - Narayanaswamy, B.
AU - Beattie, M.
AU - Kumar, B. V.K.Vijaya
AU - Savvides, M.
PY - 2006
Y1 - 2006
N2 - Distortion-tolerant correlation filter methods have been applied to many video-based automatic target recognition (ATR) applications, but in a single-frame architecture. In this paper we introduce an efficient framework for combining information from multiple correlation outputs in a probabilistic way. Our framework is capable of handling scenes with an unknown number of targets at unknown positions. The main algorithm in our framework uses a probabilistic mapping of the correlation outputs and takes advantage of a position-independent target motion model in order to efficiently compute posterior target location probabilities. An important feature of the framework is the ability to incorporate any existing correlation filter design, thus facilitating the construction of a distortion-tolerant multi-frame ATR. In our simulations, we incorporate the minimum average correlation energy Mellin radial harmonic (MACE-MRH) correlation filter design, which allows the user to specify the desired scale response of the filter. We test our algorithm on real and synthesized infrared (IR) video sequences that exhibit various degrees of target scale distortion. Our simulation results show that the multi-frame algorithm significantly improves the recognition performance of a MACE-MRH filter while requiring only a marginal increase in computation. We also show that, for an equivalent amount of added computation, using larger filter banks instead of multi-frame information is unable to provide a comparable performance increase.
AB - Distortion-tolerant correlation filter methods have been applied to many video-based automatic target recognition (ATR) applications, but in a single-frame architecture. In this paper we introduce an efficient framework for combining information from multiple correlation outputs in a probabilistic way. Our framework is capable of handling scenes with an unknown number of targets at unknown positions. The main algorithm in our framework uses a probabilistic mapping of the correlation outputs and takes advantage of a position-independent target motion model in order to efficiently compute posterior target location probabilities. An important feature of the framework is the ability to incorporate any existing correlation filter design, thus facilitating the construction of a distortion-tolerant multi-frame ATR. In our simulations, we incorporate the minimum average correlation energy Mellin radial harmonic (MACE-MRH) correlation filter design, which allows the user to specify the desired scale response of the filter. We test our algorithm on real and synthesized infrared (IR) video sequences that exhibit various degrees of target scale distortion. Our simulation results show that the multi-frame algorithm significantly improves the recognition performance of a MACE-MRH filter while requiring only a marginal increase in computation. We also show that, for an equivalent amount of added computation, using larger filter banks instead of multi-frame information is unable to provide a comparable performance increase.
UR - http://www.scopus.com/inward/record.url?scp=33747497340&partnerID=8YFLogxK
U2 - 10.1117/12.665553
DO - 10.1117/12.665553
M3 - Conference contribution
AN - SCOPUS:33747497340
SN - 0819463019
SN - 9780819463012
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Optical Pattern Recognition XVII
T2 - Optical Pattern Recognition XVII
Y2 - 20 April 2006 through 21 April 2006
ER -