Dense linear-time correspondences for tracking

A novel method is proposed for the problem of frame-to-frame correspondence search in video sequences. The method, based on hashing of low-dimensional image descriptors, establishes dense correspondences and allows large motions. All image pixels are considered for matching, the notion of interest points is reviewed. In our formulation, points of interest are those that can be reliably matched. Their saliency depends on properties of the chosen matching function and on actual image content. Both computational time and memory requirements of the correspondence search are asymptotically linear in the number of image pixels, irrespective of correspondence density and of image content. All steps of the method are simple and allow for a hardware implementation. Functionality is demonstrated on sequences taken from a vehicle moving in an urban environment.

[1]  Vincent Lepetit,et al.  Monocular Model-Based 3D Tracking of Rigid Objects: A Survey , 2005, Found. Trends Comput. Graph. Vis..

[2]  P. Anandan,et al.  A unified approach to moving object detection in 2D and 3D scenes , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[3]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[4]  P. Yip,et al.  Discrete Cosine Transform: Algorithms, Advantages, Applications , 1990 .

[5]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[6]  D. Sagi,et al.  Gabor filters as texture discriminator , 1989, Biological Cybernetics.

[7]  Hans P. Morevec.  Towards automatic visual obstacle avoidance , 1977, IJCAI 1977.

[8]  Hans P. Moravec.  Towards Automatic Visual Obstacle Avoidance , 1977, IJCAI.

[9]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[10]  Stepán Obdrzálek,et al.  Image Retrieval Using Local Compact DCT-Based Representation , 2003, DAGM-Symposium.

[11]  Gang Hua,et al.  Discriminant Embedding for Local Image Descriptors , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[13]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[14]  Peter I. Corke,et al.  Omnidirectional visual odometry for a planetary rover , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[15]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[16]  P. Anandan,et al.  A computational framework and an algorithm for the measurement of visual motion , 1987, International Journal of Computer Vision.

[17]  Larry H. Matthies,et al.  Two years of Visual Odometry on the Mars Exploration Rovers , 2007, J. Field Robotics.

[18]  Larry Matthies,et al.  Two years of Visual Odometry on the Mars Exploration Rovers: Field Reports , 2007 .