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    Tracking Isolated Objects using Affine Structure
 

Description:

We detect corners (point features) in the images and track these using Kalman filtering and affine structure. Our work attempts to unify feature tracking and structure computation and provide simultaneous solutions to both, each of which aids the other. We use a constant image velocity Kalman filter based method (proposed by Reid and Murray) to obtain the initial correspondences and subsequently impose the structure based constraints to improve the correspondence results. Such an approach improves upon the performance of the Kalman filter based correlation tracker with minimal overheads and enhances the quality of correspondences making the structure and motion computations more accurate. Using the structure and motion we localize the fixation point in each frame more accurately, and the gaze demand generated is smooth and immune to occlusion or disappearance of the corners. We have real-time (25 Hz) implementations of the tracking algorithm on a transputer based multiprocessor system, SUN Ultra Sparc and Pentium's. Currently we are developing a real-time operating system for such real-time vision systems as a part of the embedded systems project. We show some Mpeg clips below.

Tracking a toy train (what the camera sees):

Clip 1 (1.0 MByte Mpeg)
Clip 2 (1.6 MByte Mpeg)
Clip 3 (1.1 MByte Mpeg)

How the camera moves while tracking the toy train:

Clip (1.3 MByte Mpeg)

Head tracking:

Clip (0.4 MByte Mpeg)

Reports:

  1. Object Tracking using Affine Structure for Point Correspondences.
    Gurmeet Singh Manku, Pankaj Jain, Amit Aggarwal, Lalit Kumar and Subhashis Banerjee.
    IEEE CVPR'97, June 19-21, San Juan, Puerto Rico, 1997.
  2. B. Tech. Project Report of Amit Aggarwal and Lalit Kumar.
  3. Finding Point Correspondences in Motion Sequences Preserving Affine Structure.
    G. Sudhir, Subhashis Banerjee and Andrew Zisserman.
    Computer Vision and Image Understanding, Vol. 68, No. 2, pp. 237-246, November, 1997.