Understanding object motion

Many types of common objects, such as tools and vehicles, usually move in simple ways when they are wielded or driven: the natural axes of the object tend to remain aligned with the local trihedron defined by the object's trajectory. Based on this observation we use a model called Frenet-Serret motion which corresponds to the motion of a moving trihedron along a space curve. Knowing how the Frenet-Serret frame is changing relative to the observer gives us essential information for understanding the object's motion. This is illustrated here for four examples, involving tools (a wrench and a saw) and vehicles (an accelerating van, a turning taxi).

[1]  David C. Hogg,et al.  Learning the distribution of object trajectories for event recognition , 1996, Image Vis. Comput..

[2]  Narendra Ahuja,et al.  3-D Motion Estimation, Understanding, and Prediction from Noisy Image Sequences , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Hans-Hellmut Nagel,et al.  Algorithmic characterization of vehicle trajectories from image sequences by motion verbs , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Azriel Rosenfeld,et al.  Image Sequence Stabilization in Real Time , 1996, Real Time Imaging.

[5]  Mubarak Shah,et al.  Motion-based recognition a survey , 1995, Image Vis. Comput..

[6]  Rama Chellappa,et al.  Estimation of Object Motion Parameters from Noisy Images , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Hilary Buxton,et al.  Watching behaviour: the role of context and learning , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[8]  Ehud Rivlin,et al.  Function From Motion , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Rama Chellappa,et al.  Time-to-X: analysis of motion through temporal parameters , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Kevin W. Bowyer,et al.  GRUFF-3: Generalizing the domain of a function-based recognition system , 1994, Pattern Recognit..

[11]  Ruzena Bajcsy,et al.  Interactive Recognition and Representation of Functionality , 1995, Comput. Vis. Image Underst..

[12]  Mubarak Shah,et al.  The trajectory primal sketch: a multi-scale scheme for representing motion characteristics , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Alfred M. Bruckstein,et al.  How to Track a Flying Saucer , 1996, J. Vis. Commun. Image Represent..

[14]  Kevin W. Bowyer,et al.  Generic Recognition of Articulated Objects through Reasoning about Potential Function , 1995, Comput. Vis. Image Underst..

[15]  Alfred M. Bruckstein,et al.  How to Catch a Crook , 1994, J. Vis. Commun. Image Represent..

[16]  Alessandro Verri,et al.  Against Quantitative Optical Flow , 1987 .