Abstract Active vision had emerged as a new paradigm [l, 91 which integrates vision with behaviour by having control over the camera parameters. In order to solve a vision task, an active vision system has the ability to actively scan the changing environment to extract necessary information. Only a portion of an image tenned the region of interest is processed to extract information. The process of locating the region of interest for high resolution processing is called foveation. Most of the current active vision systems have a uniform low resolution image and have control over the camera parameters, in this case zooming, to obtain high resolution data. Unfortunately, zooming results in a small viewing angle and requires more mechanical operations. I( is important to have a wide viewing angle as it covers a 1015-4651/96 $5.00 0 1996 IEEE Proceedings of ICPR '96 larger field of view allowing the search for regions of interest. Most of the current active vision systems either i) have one window which switches between a wide viewing angle looking for regions of interest and zoom in for high resolution small viewing angle processing; or ii) have two windows active at all times, one window for small viewing angle high resolution processing, and another window for wide viewing angle looking for regions of interest (motion detection). Alternatively, the implementation of space variant active vision systems in log polar space allows high resolution foveal viewing ,and an exponential decrease in resolution for simultaneous periphery viewing. In this paper, we address the use of tlhe log polar map in tracking single moving objects in complicated backgrounds and multiple objects tracking in simple binary images. The overall objectives of this paper an:: 1. Apply the foveation method to develop a tracking system to actively track a moving object from frame to frame continuously in a complicated background. Apply the method to track multiple objects. We have generated a simulated tracking system that uses the open loop stage developed in our foveation algorithm [6]. The tracking system tracks a moving object from frame to frame in an indoor scene. The object is tracked by considering the distance of the object with respect to the current foveation point or camera position. Gaussian noise is also added to the image to test the robustness of the algorithm. For the multiple object tracking system, we divide the log polar map into four regions: guarded region, active region, passive region and semi-visible region. At any foveation, a search is carried out in the active region to decide which object to track in the next foveation. The competition among objects to be foveated next is based on i) size of objects; ii) number of pixels each object moved; and iii) distance of objects from the fovea. The winner is the object 745
Another application that uses multifoveation is the tracking of moving objects as proposed by Lim REF .
Tracking in a space variant active vision system
Proceedings of 13th International Conference on Pattern Recognition
Proceedings of 13th International Conference on Pattern Recognition
Computer Science