# Real-Time Sidewalk Slope Calculation through Integration of GPS Trajectory and Image Data to Assist People with Disabilities in Navigation

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Relate Works

## 3. Slope Calculation Technique

#### 3.1. Estimating User’s Position on Sidewalk

#### 3.2. Finding Google Street View Images with Sidewalks

#### 3.3. Detecting Sidewalk Segment in Image

**Figure 4.**Sidewalk detection techniques: (

**a**) sidewalks detected (blue) in Google Street View; image (

**b**) segments (red) detected as sidewalks.

**Figure 5.**Sidewalk detection. The lowest line on the right image is used to calculate the angle of the sidewalk segment.

#### 3.4. Calculating Slope

**Figure 6.**Examples of straight line detection. The slope of the sidewalk segment is the angle between the line representing the sidewalk segment and the lines representing the building.

_{1}and P

_{2}denote two points on the edge of a sidewalk segment and Q

_{1}and Q

_{2}denote two points on the edge of the background building. Then the angle of the slope can be calculated as follows:

## 4. Slope Network Profile

**Figure 7.**(

**a**) Example trajectory history for a user; (

**b**) An elevation trend curve indicating user’s pattern of slope usage.

_{d}and E

_{o}are the elevations at the destination and origin, respectively, and l is the total length of the route.

_{k}is the length of route k; l

_{min}is the length of the shortest route; Var

_{k}is the slope variance of route k; Var

_{min}is the lowest slope variance among all n options; VarHistory

_{current}is the average Var of the user; and VarHistory

_{max}is the highest average Var among all the user’s trajectories. The lower R value is for a route, the higher probability it will be recommended.

## 5. Experiment

**Figure 9.**Example sidewalk slope calculation: (

**a**) original image; (

**b**) result of Canny’s edge detection algorithm; (

**c**) result of Hough transform, five straight lines detected and shown as light spot circles; (

**d**) straight lines detected in the original image.

**Figure 11.**Example cases with incorrect or no slope calculation: (

**a**) unsuitable background data; (

**b**) unclear sidewalk segment; (

**c**) no building in background; (

**d**) background building far away.

## 6. Conclusion and Future Research

## Author Contributions

## Conflicts of Interest

## References

- Thapal, N.; Warner, G.; Drainoni, M.L.; Williams, S.R.; Ditchfield, H.; Wierbicky, J.; Nesathurai, S. A pilot study of functional access to public building and facilities for persons with impairments. Disabil. Rehabil.
**2004**, 26, 280–289. [Google Scholar] [CrossRef] [PubMed] - Meyers, A.R.; Anderson, J.J.; Miller, D.R.; Shipp, K.; Hoenig, H. Barriers, facilitators, and access for wheelchair users: Substantive and methodologic lessons from a pilot study of environmental effects. Soc. Sci. Med.
**2002**, 55, 1435–1446. [Google Scholar] [CrossRef] [PubMed] - Smith, V.; Malik, J.; Culler, D. Classification of sidewalks in street view images. In Proceedings of the 2013 International Green Computing Conference (IGCC), Arlington, VA, USA, 27–29 June 2013; pp. 1–6.
- Senlet, T.; Elgammal, A. Segmentation of occluded sidewalks in satellite images. In Proceedings of the 2012 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, 11–15 November 2012; pp. 805–808.
- Frackelton, A.; Grossman, A.; Palinginis, E.; Castrillon, F.; Elango, V.; Guensler, R. Measuring walkability: Development of an automated sidewalk quality assessment tool. Suburb. Sustain.
**2013**. [Google Scholar] [CrossRef] - Ren, M.; Karimi, H.A. Multisensor map matching for pedestrian and wheelchair navigation. In Advanced Location-Based Technologies and Services; CRC Press: Boca Raton, FL, USA, 2013; pp. 209–234. [Google Scholar]
- Greenfeld, J.S. Matching GPS observations to locations on a digital map. In Proceedings of the 81st Annual Meeting of the Transportation Research Board, Washington, DC, USA, 13–17 January 2002.
- Bernstein, D.; Kornhauser, A. An Introduction to Map Matching for Personal Navigation Assistants; New Jersey Institute of Technology: Newark, NJ, USA, 1998; p. 16. [Google Scholar]
- Karimi, H.A.; Zhang, L.; Benner, J.G. Personalized Accessibility Map (PAM): A novel assisted wayfinding approach for people with disabilities. Ann. GIS
**2014**, 20, 99–108. [Google Scholar] [CrossRef] - Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell.
**1986**, 8, 679–698. [Google Scholar] [CrossRef] [PubMed] - Ballard, D.H. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit.
**1981**, 13, 111–122. [Google Scholar] [CrossRef] - Duda, R.O.; Hart, P.E. Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM
**1972**, 15, 11–15. [Google Scholar] [CrossRef]

© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Lu, Y.; Karimi, H.A.
Real-Time Sidewalk Slope Calculation through Integration of GPS Trajectory and Image Data to Assist People with Disabilities in Navigation. *ISPRS Int. J. Geo-Inf.* **2015**, *4*, 741-753.
https://doi.org/10.3390/ijgi4020741

**AMA Style**

Lu Y, Karimi HA.
Real-Time Sidewalk Slope Calculation through Integration of GPS Trajectory and Image Data to Assist People with Disabilities in Navigation. *ISPRS International Journal of Geo-Information*. 2015; 4(2):741-753.
https://doi.org/10.3390/ijgi4020741

**Chicago/Turabian Style**

Lu, Yihan, and Hassan. A. Karimi.
2015. "Real-Time Sidewalk Slope Calculation through Integration of GPS Trajectory and Image Data to Assist People with Disabilities in Navigation" *ISPRS International Journal of Geo-Information* 4, no. 2: 741-753.
https://doi.org/10.3390/ijgi4020741