Stereo vision based motion identification

Authors:
Xinkai Chen
Affiliations:
Department of Electronic and Information Systems, Shibaura Institute of Technology, Saitama-shi, Saitama, Japan
Venue:
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Year:
2007

Citing 8
Cited 0

Adaptive control: stability, convergence, and robustness

Adaptive control: stability, convergence, and robustness
A necessary and sufficient condition for the perspective observability problem

Systems & Control Letters
Visually guided ranging from observations of points, lines and curves via an identifier based nonlinear observer

Systems & Control Letters
3-D structure from visual motion: modeling, representation and observability

Automatica (Journal of IFAC)
Group Theoretical Methods in Image Understanding

Group Theoretical Methods in Image Understanding
Recursive Estimation of Motion, Structure, and Focal Length

IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure from Motion Causally Integrated Over Time

IEEE Transactions on Pattern Analysis and Machine Intelligence
Brief Paper: An EKF-Based Nonlinear Observer with a Prescribed Degree of Stability

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

Abstract

The motion identification for a class of movements in the space by using stereo vision is considered by observing at least three points in this paper. The considered motion equation can cover a wide class of practical movements in the space. The observability of this class of movement is clarified. The estimations of the motion parameters which are all time-varying are developed in the proposed algorithm based on the second method of Lyapunov. The gence assumptions about the perspective system are reasonable, and the conver-conditions are intuitive and have apparently physical interpretations. The proposed recursive algorithm requires minor a priori knowledge about the system and can alleviate the noises in the image data. Furthermore, the proposed algorithm is modified to deal with the occlusion phenomenon. Simulation results show the proposed algorithm is effective even in the presence of measurement noises.