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Active Visual Search

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Absract: 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.

About the speaker: Professor John K. Tsotsos, Fellow of the Royal Society of Canada and Distinguished Research Professor of Vision Science, holds the Canada Research Chair in Computational Vision at York University, as well as Adjust professorships at the Department of Ophthalmology and Visions Sciences at the University of Toronto. His research focuses on three main themes: visual attention in humans and computer systems, visually guided mobile robotics and computer vision. He has published numerous papers in multiple disciplines including computer science, attention, computational neuroscience, computer vision and robotics, and he is the author of an upcoming MIT press book “A Computational Perspective on Visual Attention”.

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