Camera-based signage detection and recognition for blind persons

Shuihua Wang; Yingli Tian

doi:10.1007/978-3-642-31534-3_3

Camera-based signage detection and recognition for blind persons

Shuihua Wang^*, Yingli Tian

^*Corresponding author for this work

City University of New York

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

19 Citations (Scopus)

Abstract

Signage plays an important role for wayfinding and navigation to assist blind people accessing unfamiliar environments. In this paper, we present a novel camera-based approach to automatically detect and recognize restroom signage from surrounding environments. Our method first extracts the attended areas which may content signage based on shape detection. Then, Scale-Invariant Feature Transform (SIFT) is applied to extract local features in the detected attended areas. Finally, signage is detected and recognized as the regions with the SIFT matching scores larger than a threshold. The proposed method can handle multiple signage detection. Experimental results on our collected restroom signage dataset demonstrate the effectiveness and efficiency of our proposed method.

Original language	English
Title of host publication	Computers Helping People with Special Needs - 13th International Conference, ICCHP 2012, Proceedings
Pages	17-24
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-31534-3_3
Publication status	Published - 2012
Externally published	Yes
Event	13th International Conference on Computers Helping People with Special Needs, ICCHP 2012 - Linz, Austria Duration: 11 Jul 2012 → 13 Jul 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7383 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Computers Helping People with Special Needs, ICCHP 2012
Country/Territory	Austria
City	Linz
Period	11/07/12 → 13/07/12

Keywords

Blind people
Navigation and wayfinding
Signage detection and recognition

Access to Document

10.1007/978-3-642-31534-3_3

Cite this

Wang, S., & Tian, Y. (2012). Camera-based signage detection and recognition for blind persons. In Computers Helping People with Special Needs - 13th International Conference, ICCHP 2012, Proceedings (PART 2 ed., pp. 17-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7383 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-31534-3_3

Wang, Shuihua ; Tian, Yingli. / Camera-based signage detection and recognition for blind persons. Computers Helping People with Special Needs - 13th International Conference, ICCHP 2012, Proceedings. PART 2. ed. 2012. pp. 17-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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abstract = "Signage plays an important role for wayfinding and navigation to assist blind people accessing unfamiliar environments. In this paper, we present a novel camera-based approach to automatically detect and recognize restroom signage from surrounding environments. Our method first extracts the attended areas which may content signage based on shape detection. Then, Scale-Invariant Feature Transform (SIFT) is applied to extract local features in the detected attended areas. Finally, signage is detected and recognized as the regions with the SIFT matching scores larger than a threshold. The proposed method can handle multiple signage detection. Experimental results on our collected restroom signage dataset demonstrate the effectiveness and efficiency of our proposed method.",

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author = "Shuihua Wang and Yingli Tian",

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Wang, S & Tian, Y 2012, Camera-based signage detection and recognition for blind persons. in Computers Helping People with Special Needs - 13th International Conference, ICCHP 2012, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7383 LNCS, pp. 17-24, 13th International Conference on Computers Helping People with Special Needs, ICCHP 2012, Linz, Austria, 11/07/12. https://doi.org/10.1007/978-3-642-31534-3_3

Camera-based signage detection and recognition for blind persons. / Wang, Shuihua; Tian, Yingli.
Computers Helping People with Special Needs - 13th International Conference, ICCHP 2012, Proceedings. PART 2. ed. 2012. p. 17-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7383 LNCS, No. PART 2).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AB - Signage plays an important role for wayfinding and navigation to assist blind people accessing unfamiliar environments. In this paper, we present a novel camera-based approach to automatically detect and recognize restroom signage from surrounding environments. Our method first extracts the attended areas which may content signage based on shape detection. Then, Scale-Invariant Feature Transform (SIFT) is applied to extract local features in the detected attended areas. Finally, signage is detected and recognized as the regions with the SIFT matching scores larger than a threshold. The proposed method can handle multiple signage detection. Experimental results on our collected restroom signage dataset demonstrate the effectiveness and efficiency of our proposed method.

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KW - Navigation and wayfinding

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Wang S, Tian Y. Camera-based signage detection and recognition for blind persons. In Computers Helping People with Special Needs - 13th International Conference, ICCHP 2012, Proceedings. PART 2 ed. 2012. p. 17-24. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-31534-3_3

Camera-based signage detection and recognition for blind persons

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