Active Visual Search

John K. Tsotsos

(York University, Canada)

(INVITED RIVIC GUEST)

Active perception uses intelligent control strategies applied to the data acquisition process that depend on the current state of data interpretation and has a history that pre-dates computer vision. I will very briefly lay out this history and detail theoretical arguments on the computational nature of the general problem. The theory informs us that optimal solutions are not likely to exist. In this context, I consider the problem of visually finding an object in a mostly unknown space with a mobile robot. It is clear that all possible views and images cannot be examined in a practical system and as a result, this is cast as an optimization problem. The goal is to optimize the probability of finding the target given a fixed cost limit in terms of total number of robotic actions required to find the visual target. Due to the inherent intractability of this problem, we present an approximate solution and investigate its performance and properties. This has been successfully implemented on three different robots, the most recent being Honda's ASIMO and examples of its performance will be shown.


If anyone wishes to see background papers for this, they can look at:

Andreopoulos, A., Wersing, H., Janssen, H., Hasler, S., Tsotsos, J.K., K├Ârner, E., Active 3D Object Localization using a Humanoid RObot, IEEE Transactions on Robotics, 27(1), p47-64, 2011.

Shubina, K., Tsotsos, J.K. Visual Search for an Object in a 3D Environment using a Mobile Robot, Computer Vision and Image Understanding, 114, p535-547, 2010.

Ye, Y., Tsotsos, J.K., A Complexity Level Analysis of the Sensor Planning Task for Object Search, Computational Intelligence, 17(4), p605-620, Nov. 2001.

Ye, Y., Tsotsos, J.K., Sensor Planning for Object Search, Computer Vision and Image Understanding 73(2), p145-168, 1999.
Wednesday 25th May 2011, 12:30
Robert Recorde Room
Department of Computer Science