Title :
A non-myopic approach to visual search
Author :
Vogel, Julia ; Murphy, Kevin
Author_Institution :
Univ. of British Columbia, Vancouver
Abstract :
We show how a greedy approach to visual search - i.e., directly moving to the most likely location of the target - can be suboptimal, if the target object is hard to detect. Instead it is more efficient and leads to higher detection accuracy to first look for other related objects, that are easier to detect. These provide contextual priors for the target that make it easier to find. We demonstrate this in simulation using POMDP models, focussing on two special cases: where the target object is contained within the related object, and where the target object is spatially adjacent to the related object.
Keywords :
Markov processes; control engineering computing; greedy algorithms; object detection; robot vision; greedy approach; nonmyopic approach; partially observed Markov decision process; robot; target object detection; visual search; Buildings; Computational modeling; Computer displays; Computer science; Computer vision; Detectors; Object detection; Robot sensing systems; Robot vision systems; Uncertainty;
Conference_Titel :
Computer and Robot Vision, 2007. CRV '07. Fourth Canadian Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7695-2786-8