TY - GEN
T1 - An evaluation of attention models for use in SLAM
AU - Dodge, Samuel
AU - Karam, Lina
PY - 2014
Y1 - 2014
N2 - In this paper we study the application of visual saliency models for the simultaneous localization and mapping (SLAM) problem. We consider visual SLAM, where the location of the camera and a map of the environment can be generated using images from a single moving camera. In visual SLAM, the interest point detector is of key importance. This detector must be invariant to certain image transformations so that features can be matched across di erent frames. Recent work has used a model of human visual attention to detect interest points, however it is unclear as to what is the best attention model for this purpose. To this aim, we compare the performance of interest points from four saliency models (Itti, GBVS, RARE, and AWS) with the performance of four traditional interest point detectors (Harris, Shi-Tomasi, SIFT, and FAST). We evaluate these detectors under several di erent types of image transformation and nd that the Itti saliency model, in general, achieves the best performance in terms of keypoint repeatability.
AB - In this paper we study the application of visual saliency models for the simultaneous localization and mapping (SLAM) problem. We consider visual SLAM, where the location of the camera and a map of the environment can be generated using images from a single moving camera. In visual SLAM, the interest point detector is of key importance. This detector must be invariant to certain image transformations so that features can be matched across di erent frames. Recent work has used a model of human visual attention to detect interest points, however it is unclear as to what is the best attention model for this purpose. To this aim, we compare the performance of interest points from four saliency models (Itti, GBVS, RARE, and AWS) with the performance of four traditional interest point detectors (Harris, Shi-Tomasi, SIFT, and FAST). We evaluate these detectors under several di erent types of image transformation and nd that the Itti saliency model, in general, achieves the best performance in terms of keypoint repeatability.
KW - Interest Points
KW - SLAM
KW - Saliency
KW - Visual Attention
UR - http://www.scopus.com/inward/record.url?scp=84896790536&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896790536&partnerID=8YFLogxK
U2 - 10.1117/12.2043042
DO - 10.1117/12.2043042
M3 - Conference contribution
AN - SCOPUS:84896790536
SN - 9780819499424
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Proceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXXI
T2 - Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques
Y2 - 4 February 2014 through 6 February 2014
ER -