Closing the loop: detection and pursuit of a moving object by a moving observer

Abstract We present an integrated system which is able to pursue a moving object and maintain the object centered in the image by controlling a robot-head. The system contains three parts: independent motion detection, tracking, and control of a robot-head. This paper focuses on the detection mechanism, and briefly discusses the tracking and control issues. The system runs continuously in time and updates the object localization at a frame-rate of 25 Hz. The moving object can be tracked, although the observer performs an unknown independent motion, involving both translation and rotation. We focus on a simple motion detection algorithm, since computational cost is of major importance for real-time systems with feedback. The algorithm is noniterative and computationally inexpensive. The running time complexity is O ( n ) for an image containing n pixels. The image-processing takes place on a MaxVideo 200 pipeline processor, and the head control algorithm is running on a Transputer T800 network. Some offline experimental results are presented, where comparisons are made between affine and translational image motion models.

[1]  Ian D. Reid,et al.  Saccade and pursuit on an active head/eye platform , 1994, Image Vis. Comput..

[2]  P. McLeod,et al.  Filtering by movement in visual search. , 1991, Journal of experimental psychology. Human perception and performance.

[3]  A. Treisman.  Features and Objects: The Fourteenth Bartlett Memorial Lecture , 1988, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[4]  Jan-Olof Eklundh,et al.  Mechatronics of active vision , 1994 .

[5]  Rama Chellappa,et al.  Motion-model-based boundary extraction , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[6]  Jan-Olof Eklundh,et al.  Integrating primary ocular processes , 1992, Image Vis. Comput..

[7]  Patrick Bouthemy,et al.  Region-Based Tracking Using Affine Motion Models in Long Image Sequences , 1994 .

[8]  John Wang,et al.  Applying mid-level vision techniques for video data compression and manipulation , 1994, Electronic Imaging.

[9]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[10]  S. Yantis,et al.  Abrupt visual onsets and selective attention: Evidence from visual search. , 1984 .

[11]  Anup Basu,et al.  Motion Tracking with an Active Camera , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  S. Yantis,et al.  Uniqueness of abrupt visual onset in capturing attention , 1988, Perception & psychophysics.

[13]  Michal Irani,et al.  Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency , 1993, J. Vis. Commun. Image Represent..

[14]  Atsuto Maki,et al.  Towards an active visual observer , 1995, Proceedings of IEEE International Conference on Computer Vision.

[15]  C. Enroth-Cugell,et al.  The contrast sensitivity of retinal ganglion cells of the cat , 1966, The Journal of physiology.

[16]  James L. Crowley,et al.  Vision as Process , 1995 .

[17]  S. Yantis,et al.  Abrupt visual onsets and selective attention: evidence from visual search. , 1984, Journal of experimental psychology. Human perception and performance.

[18]  Rachid Deriche,et al.  Tracking line segments , 1990, Image Vis. Comput..

[19]  Edward H. Adelson,et al.  Spatio-temporal segmentation of video data , 1994, Electronic Imaging.

[20]  D. Tolhurst.  Separate channels for the analysis of the shape and the movement of a moving visual stimulus , 1973, The Journal of physiology.