title: Recursive camera-motion estimation with the trifocal tensor.
abstract: In this paper, an innovative extended Kalman filter (EKF) algorithm for pose tracking using the trifocal tensor is proposed. In the EKF, a constant-velocity motion model is used as the dynamic system, and the trifocal-tensor constraint is incorporated into the measurement model. The proposed method has the advantages of those structure- and-motion-based approaches in that the pose sequence can be computed with no prior information on the scene structure. It also has the strengths of those model-based algorithms in which no updating of the three-dimensional (3-D) structure is necessary in the computation. This results in a stable, accurate, and efficient algorithm. Experimental results show that the proposed approach outperformed other existing EKFs that tackle the same problem. An extension to the pose-tracking algorithm has been made to demonstrate the application of the trifocal constraint to fast recursive 3-D structure recovery.
authors.affiliation: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong. ykyu@cse.cuhk.edu.hk
authors.forename: Ying Kin
authors.initials: YK
authors.lastname: Yu
authors.forename: Kin Hong
authors.initials: KH
authors.lastname: Wong
authors.forename: Michael Ming Yuen
authors.initials: MM
authors.lastname: Chang
authors.forename: Siu Hang
authors.initials: SH
authors.lastname: Or
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D010780:Photogrammetry / Q000379:methods*; D012815:Signal Processing, Computer-Assisted