Toward real-time grocery detection for the visually impaired

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

Toward real-time grocery detection for the visually impaired. / Winlock, Tess; Christiansen, Eric; Belongie, Serge.

In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, 2010, p. 49-56.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Winlock, T, Christiansen, E & Belongie, S 2010, 'Toward real-time grocery detection for the visually impaired', 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, pp. 49-56. https://doi.org/10.1109/CVPRW.2010.5543576

APA

Winlock, T., Christiansen, E., & Belongie, S. (2010). Toward real-time grocery detection for the visually impaired. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, 49-56. https://doi.org/10.1109/CVPRW.2010.5543576

Vancouver

Winlock T, Christiansen E, Belongie S. Toward real-time grocery detection for the visually impaired. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010. 2010;49-56. https://doi.org/10.1109/CVPRW.2010.5543576

Author

Winlock, Tess ; Christiansen, Eric ; Belongie, Serge. / Toward real-time grocery detection for the visually impaired. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010. 2010 ; pp. 49-56.

Bibtex

@inproceedings{92979e4fefeb4de3a4d07273d0a5d1f7,
title = "Toward real-time grocery detection for the visually impaired",
abstract = "We present a study on grocery detection using our object detection system, Shelf Scanner, which seeks to allow a visually impaired user to shop at a grocery store without additional human assistance. Shelf Scanner allows online detection of items on a shopping list, in video streams in which some or all items could appear simultaneously. To deal with the scale of the object detection task, the system leverages the approximate planarity of grocery store shelves to build a mosaic in real time using an optical flow algorithm. The system is then free to use any object detection algorithm without incurring a loss of data due to processing time. For purposes of speed we use a multi class naive-Bayes classifier inspired by NIMBLE, which is trained on enhanced SURF descriptors extracted from images in the GroZi-120 dataset. It is then used to compute per-class probability distributions on video key points for final classification. Our results suggest Shelf Scanner could be useful in cases where high-quality training data is available.",
author = "Tess Winlock and Eric Christiansen and Serge Belongie",
year = "2010",
doi = "10.1109/CVPRW.2010.5543576",
language = "English",
pages = "49--56",
journal = "2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010",
note = "2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010 ; Conference date: 13-06-2010 Through 18-06-2010",

}

RIS

TY - GEN

T1 - Toward real-time grocery detection for the visually impaired

AU - Winlock, Tess

AU - Christiansen, Eric

AU - Belongie, Serge

PY - 2010

Y1 - 2010

N2 - We present a study on grocery detection using our object detection system, Shelf Scanner, which seeks to allow a visually impaired user to shop at a grocery store without additional human assistance. Shelf Scanner allows online detection of items on a shopping list, in video streams in which some or all items could appear simultaneously. To deal with the scale of the object detection task, the system leverages the approximate planarity of grocery store shelves to build a mosaic in real time using an optical flow algorithm. The system is then free to use any object detection algorithm without incurring a loss of data due to processing time. For purposes of speed we use a multi class naive-Bayes classifier inspired by NIMBLE, which is trained on enhanced SURF descriptors extracted from images in the GroZi-120 dataset. It is then used to compute per-class probability distributions on video key points for final classification. Our results suggest Shelf Scanner could be useful in cases where high-quality training data is available.

AB - We present a study on grocery detection using our object detection system, Shelf Scanner, which seeks to allow a visually impaired user to shop at a grocery store without additional human assistance. Shelf Scanner allows online detection of items on a shopping list, in video streams in which some or all items could appear simultaneously. To deal with the scale of the object detection task, the system leverages the approximate planarity of grocery store shelves to build a mosaic in real time using an optical flow algorithm. The system is then free to use any object detection algorithm without incurring a loss of data due to processing time. For purposes of speed we use a multi class naive-Bayes classifier inspired by NIMBLE, which is trained on enhanced SURF descriptors extracted from images in the GroZi-120 dataset. It is then used to compute per-class probability distributions on video key points for final classification. Our results suggest Shelf Scanner could be useful in cases where high-quality training data is available.

UR - http://www.scopus.com/inward/record.url?scp=77956524937&partnerID=8YFLogxK

U2 - 10.1109/CVPRW.2010.5543576

DO - 10.1109/CVPRW.2010.5543576

M3 - Conference article

AN - SCOPUS:77956524937

SP - 49

EP - 56

JO - 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010

JF - 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010

T2 - 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010

Y2 - 13 June 2010 through 18 June 2010

ER -

ID: 302048295