TY - JOUR
T1 - A generic approach to simultaneous tracking and verification in video
AU - Li, Baoxin
AU - Chellappa, Rama
N1 - Funding Information:
Manuscript received May 1, 2001; revised February 6, 2002. This work was supported by the Advanced Sensors Consortium (ASC) sponsored by the U.S. Army Research Laboratory under the Federated Laboratory Program, Cooperative Agreement DAAL01-96-2-0001. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Thiow Keng Tan.
PY - 2002/5
Y1 - 2002/5
N2 - In this paper, a generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given a measurement vector Z and two hypotheses H 1 and H 0, we first estimate posterior probabilities P(H 0|Z) and P(H 1|Z), and then choose the one with the larger posterior probability as the true hypothesis. Several applications of the approach are illustrated by experiments devised to evaluate its performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented, illustrating vehicle tracking and verification, human (face) tracking and verification, facial feature tracking, and image sequence stabilization.
AB - In this paper, a generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given a measurement vector Z and two hypotheses H 1 and H 0, we first estimate posterior probabilities P(H 0|Z) and P(H 1|Z), and then choose the one with the larger posterior probability as the true hypothesis. Several applications of the approach are illustrated by experiments devised to evaluate its performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented, illustrating vehicle tracking and verification, human (face) tracking and verification, facial feature tracking, and image sequence stabilization.
KW - Importance sampling
KW - Monte Carlo method
KW - Object verification
KW - Visual tracking
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U2 - 10.1109/TIP.2002.1006400
DO - 10.1109/TIP.2002.1006400
M3 - Article
C2 - 18244653
AN - SCOPUS:0036563562
SN - 1057-7149
VL - 11
SP - 530
EP - 544
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 5
ER -