Finding Trajectories of Feature Points in a Monocular Image Sequence

Identifying the same physical point in more than one image, the correspondence problem, is vital in motion analysis. Most research for establishing correspondence uses only two frames of a sequence to solve this problem. By using a sequence of frames, it is possible to exploit the fact that due to inertia the motion of an object cannot change instantaneously. By using smoothness of motion, it is possible to solve the correspondence problem for arbitrary motion of several nonrigid objects in a scene. We formulate the correspondence problem as an optimization problem and propose an iterative algorithm to find trajectories of points in a monocular image sequence. A modified form of this algorithm is useful in case of occlusion also. We demonstrate the efficacy of this approach considering synthetic, laboratory, and real scenes.

[1]  Ramesh C. Jain,et al.  Imprecision in Computer Vision , 1982, Computer.

[2]  Ramesh C. Jain,et al.  Detecting time-varying corners , 1984, Comput. Vis. Graph. Image Process..

[3]  Michael Brady,et al.  Parallelism in Vision , 1983, Artif. Intell..

[4]  Ramesh C. Jain,et al.  Difference and accumulative difference pictures in dynamic scene analysis , 1984, Image Vis. Comput..

[5]  A. Waxman.  An image flow paradigm , 1987 .

[6]  G Johansson,et al.  Spatio-temporal differentiation and integration in visual motion perception , 1976, Psychological research.

[7]  T. D. Williams,et al.  Depth from camera motion in a real world scene , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Michael Jenkin,et al.  Tracking three-dimensional moving light displays , 1986, Workshop on Motion.

[9]  Thomas S. Huang,et al.  Estimating three-dimensional motion parameters of a rigid planar patch , 1981 .

[10]  Ramesh C. Jain,et al.  Determining Motion Parameters for Scenes with Translation and Rotation , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Thomas S. Huang,et al.  Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects with Curved Surfaces , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  J. Todd.  Visual information about rigid and nonrigid motion: a geometric analysis. , 1982, Journal of experimental psychology. Human perception and performance.

[13]  William B. Thompson,et al.  Analysis of Accretion and Deletion at Boundaries in Dynamic Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Minoru Asada,et al.  Automatic Analysis of Moving Images , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  J. Roach,et al.  Determining the movement of objects from a sequence of images , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Ramesh C. Jain,et al.  On the Analysis of Accumulative Difference Pictures from Image Sequences of Real World Scenes , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  J. O'Rourke,et al.  Model-based image analysis of human motion using constraint propagation , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  J. H. Rieger,et al.  Determining the instantaneous axis of translation from optic flow generated by arbitrary sensot motion , 1986, Workshop on Motion.

[19]  Thomas S. Huang,et al.  Estimating three-dimensional motion parameters of a rigid planar patch, II: Singular value decomposition , 1982 .

[20]  S. Ullman.  The Interpretation of Visual Motion , 1979 .

[21]  Michael A. Arbib,et al.  Computing the optic flow: The MATCH algorithm and prediction , 1983, Comput. Vis. Graph. Image Process..

[22]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[23]  R. F. Rashid,et al.  Towards a system for the interpretation of moving light displays , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Jake K. Aggarwal,et al.  Structure from Motion of Rigid and Jointed Objects , 1981, Artif. Intell..

[25]  J S Lappin,et al.  Detection of three-dimensional structure in moving optical patterns. , 1984, Journal of experimental psychology. Human perception and performance.

[26]  Ramesh Jain,et al.  Axial motion stereo , 1984 .

[27]  Ellen C. Hildreth,et al.  Computations Underlying the Measurement of Visual Motion , 1984, Artif. Intell..

[28]  D Marr,et al.  Early processing of visual information. , 1976, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[29]  John K. Tsotsos,et al.  A framework for visual motion understanding , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  J. Gibson.  The Ecological Approach to Visual Perception , 1979 .

[31]  William B. Thompson,et al.  TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2009 .

[32]  B. Chandrasekaran,et al.  A Theory of Spatio-Temporal Aggregation for Vision , 1981, Artif. Intell..

[33]  Ramesh C. Jain,et al.  Direct Computation of the Focus of Expansion , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  V S Ramachandran,et al.  Interpolation during Apparent Motion , 1982, Perception.

[35]  Hans-Hellmut Nagel,et al.  Volumetric model and 3D trajectory of a moving car derived from monocular TV frame sequences of a street scene , 1981, Comput. Graph. Image Process..