Camera-based Signage Detection and Recognition for Blind Persons

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Portable and Mobile Systems in Assistive Technology - Camera-based Signage Detection and Recognition for Blind Persons - Tian, Yingli (f)

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  • The detection of the RFID tag is done by the RFID system it self with the reader and the antenna. The human action part or once a bottle tag is removed from the range of the anteena, computer vision tools plays in roll. Algorithms like Background subtraction and color modeling are used to detect those moving bottle tags ONLY.Since the computation time is very long, we used multi thread programming in order to reduce computation time. Also as for the color detection, the HSV color model is used to detect the tags after the background subtraction model has done its job.We use HSV, because HSV describes color using more familiar comparisons such as color, vibrancy and brightness. And the tag color is just only pure Blue…
  • Camera-based Signage Detection and Recognition for Blind Persons

    1. 1. Shuihua Wang and Yingli Tian {swang15, ytian}@ccny.cuny.edu Presented by: Shizhi ChenDepartment of Electrical Engineering The City College of New York
    2. 2. Outline Motivation Proposed algorithm Experimental results
    3. 3. Motivation Access unfamiliar environment Recognize restroom signage Available technology (a) (b) (c)
    4. 4. Proposed Algorithm
    5. 5. Image PreprocessOriginal Gray Binary Connected Image Image Image Components
    6. 6. Signage Detection: Head Based on shape of Connected Components (CC) Head shape is circle
    7. 7. Signage Detection: Body More variations Close to head Based on shape
    8. 8. Signage Detection Results Scale invariant Rotation invariant Illumination invariant
    9. 9. Signage Recognition: Find Corners Search within detected signage region SIFT (Scale Invariant Feature Transform) detector Search over all scales Template Signage Detected Signage
    10. 10. Signage Recognition: Match Corners SIFT descriptor  Histogram of gradients  Rotation and scale invariant Matching pair of corners: minimal Euclidean distance Find the template with maximum matching pairs Template Signage Detected Signage
    11. 11. Signage Database 102 Signage: Men (50); Women(42); Disabled(10)
    12. 12. Experiment Results 89.2% detection rate (91 out of 102 images) 84.3% recognition rate (86 out of 102 images) Confusion matrix: column is the ground truth
    13. 13. Intermediate Results Original Image Binary Image Connected Component W D M D W M Signage Recognition
    14. 14. Recognition Success M M M W M M W W W W W W D M D D D W
    15. 15. Detection Fails Significant view angle changes Complex Background
    16. 16. Acknowledgement Supported: NIH 1R21EY020990, NSF grants IIS-0957016 EFRI-1137172. 16
    17. 17. Thank you!
    18. 18. Author Contact Shuihua Wang and Yingli Tian {swang15, ytian}@ccny.cuny.edu

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