Optical flow based obstacle detection in static environment

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science)

Volumn 42, Issue 6, 2008, Pages 923-926

  Zhu, Yun Fang ^b   Wang, Yi Shu ^a   Du, Xin ^a  

^a ZHEJIANG GONGSHANG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

Author keywords

Obstacle detection; Optical flow; Time to contact

Indexed keywords

ERROR ANALYSIS; ESTIMATION; OBSTACLE DETECTORS; WIRELESS SENSOR NETWORKS;

FIRST ORDER DERIVATIVES (FOD); FLOW BASED; HOMOGRAPHY; IMAGE NOISE; IMAGE SEQUENCING; MONOCULAR CAMERAS; NOVEL METHODS; OBSTACLE DETECTION; OPTICAL (PET) (OPET); OUTDOOR SCENES; RANDOM SAMPLE CONSENSUS (RANSAC); SCALE-INVARIANT FEATURE TRANSFORM (SIFT); STATIC ENVIRONMENTS; TIME-TO-CONTACT;

OPTICAL FLOWS;

EID: 47549117962     PISSN: 1008973X     EISSN: None     Source Type: Journal    
DOI: 10.3785/j.issn.1008-973X.2008.06.005     Document Type: Article

References (19)

1. LOURAKIS M I A, ORPHANOUDAKIS S C. Using planar parallax to estimate the time-to-contact [C]//IEEE Conference on Computer Vision and Pattern Recognition. Fort Collins, CO, USA: IEEE, 1999, 2: 639-645.
2. HORRIDGE G A. The evolution of visual processing and the construction of seeing systems [C]//Proceedings of the Royal Society of London. London: Royal Society of London, 1987, 230: 279-292.
3. SUBBARAO M. Bounds on time-to-collision and rotational component from first-order derivatives of image flow [J]. Computer Vision Graphics and Image Processing, 1990, 50: 329-341.
4. TISTARELLI M, SANDINI G. On the advantages of polar and log-polar mappings for direct estimation of time-to-impact from optical flow [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(4): 401-410.
5. MEYER F G. Time-to-collision from first-order models of the motion field [J]. IEEE Transactions on Robotics and Automation, 1994, 10(6): 792-798.
6. LOURAKIS M I A, ORPHANOUDAKIS S C. Visual detection of obstacles assuming a locally planar ground [C]//Proceedings of the Third Asian Conference on Computer Vision. Hong Kong: Springer-Verlag, 1998, 2: 527-534.
7. HORN B K P, SCHUNCK B G. Determining optical flow [J]. Artificial Intelligence, 1981, 17: 185-204.
8. LUCAS B, KANADE T. An iterative image registration technique with an application to stereo vision [C]//Proceedings of the 7th International Joint Conference on Artificial Intelligence. Vancouver: [s. n.], 1981: 674-679.
9. FLEET D J, JEPSON A D. Computation of component image velocity from local phase information [J]. International Journal of Computer Vision, 1990, 5(1): 77-104.
10. SHI J, TOMASI G. Good features to track [C]//IEEE Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 1994: 527-534.
11. BERTERO M, POGGIO T A, TORRE V. Ill-posed problems in early vision [J]. Proceedings of the IEEE. 1988, 76(8): 869-889.
12. LOWE D G J. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
13. FISCHLER M A, BOLLES R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography [J]. Communications of the ACM, 1981, 24: 381-395.
14. GOLUB G H, VAN LOAN C F. Matrix computations [M]. Baltimore: Johns Hopkins University Press, 1996.
15. SUBBARAO M, WAXMAN A. Closed form solutions to image flow equations for planar surfaces in motion [J]. Computer Vision, Graphics and Image Processing, 1986, 36: 208-228.
16. ONGUET-HIGGINS H C. The visual ambiguity of a moving plane [C]//Proceedings of the Royal Society of London. London: Royal Society of London, 1984, 223: 165-175.
17. MORE J. Levenberg-Marquardt algorithm: implementation and theory [C]//Conference on Numerical Analysis. Dundee, UK: [s. n.], 1977: 105-116.
18. BROWNING B. Biologically plausible spatial navigation for a mobile robot [D]. Australia: University of Queensland, 2000.