ABSTRACT
Research in social science has shown that mobile phone conversations distract users, presenting a significant impact to pedestrian safety; for example, a mobile phone user deep in conversation while crossing a street is generally more at risk than other pedestrians not engaged in such behavior. We propose WalkSafe, an Android smartphone application that aids people that walk and talk, improving the safety of pedestrian mobile phone users. WalkSafe uses the back camera of the mobile phone to detect vehicles approaching the user, alerting the user of a potentially unsafe situation; more specifically WalkSafe i) uses machine learning algorithms implemented on the phone to detect the front views and back views of moving vehicles and ii) exploits phone APIs to save energy by running the vehicle detection algorithm only during active calls. We present our initial design, implementation and evaluation of the WalkSafe App that is capable of real-time detection of the front and back views of cars, indicating cars are approaching or moving away from the user, respectively. WalkSafe is implemented on Android phones and alerts the user of unsafe conditions using sound and vibration from the phone. WalkSafe is available on Android Market.
- G. Bradski and A. Kaehler. Learning OpenCV. O'Reilly Media, 2008.Google Scholar
- F. Bu and C.-Y. Chan. Pedestrian detection in transit bus application: sensing technologies and safety solutions. In Proc. of the IEEE Intelligent Vehicles Symposium, pages 100--105, June 2005.Google ScholarCross Ref
- Computational Vision Group at Caltech. Cars 2001 (rear), 2001. http://www.vision.caltech.edu/html-files/archive.html.Google Scholar
- K. David and A. Flach. Car-2-x and pedestrian safety. Vehicular Technology Magazine, IEEE, 5(1):70--76, March 2010.Google ScholarCross Ref
- T. Gandhi and M. Trivedi. Pedestrian protection systems: Issues, survey, and challenges. IEEE Transactions on Intelligent Transportation Systems, 8(3):413--430, September 2007. Google ScholarDigital Library
- D. Gavrila. Sensor-based pedestrian protection. IEEE Intelligent Systems, 16(6):77--81, 2001. Google ScholarDigital Library
- Governors Highway Safety Association. Pedestrian traffic fatalities by state: 2010 preliminary data, http://www.ghsa.org, 2010.Google Scholar
- J. Hatfield and S. Murphy. The effects of mobile phone use on pedestrian crossing behaviour at signalised and unsignalised intersections. Accident Analysis & Prevention, 39(1):197--205, 2007.Google ScholarCross Ref
- N. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. Campbell. A survey of mobile phone sensing. Communications Magazine, IEEE, 48(9):140--150, 2010. Google ScholarDigital Library
- J. Nasar, P. Hecht, and R. Wener. Mobile telephones, distracted attention, and pedestrian safety. Accident Analysis & Prevention, 40(1):69--75, 2008.Google ScholarCross Ref
- National Highway Traffic Safety Administration. Traffic safety facts 2009. Washington, DC, 2010.Google Scholar
- M. B. Neider, J. S. McCarley, J. A. Crowell, H. Kaczmarski, and A. F. Kramer. Pedestrians, vehicles, and cell phones. Accident Analysis & Prevention, 42(2):589--594, 2010.Google ScholarCross Ref
- Oki Electric Industry. Oki succeeds in trial production of world's first "safety mobile phone" to improve pedestrian safety, May 2007. http://www.oki.com/en/press/2007/z07023e.html.Google Scholar
- E. Ophir, C. Nass, and A. D. Wagner. Cognitive control in media multitaskers. Proc. of the National Academy of Sciences, 106(37):15584--15587, 2009.Google ScholarCross Ref
- C. Papageorgiou and T. Poggio. A trainable object detection system: Car detection in static images. Technical Report 1673, October 1999. (CBCL Memo 180).Google Scholar
- S. Sivaraman and M. Trivedi. A general active-learning framework for on-road vehicle recognition and tracking. IEEE Transactions on Intelligent Transportation Systems, 11(2):267--276, June 2010. Google ScholarDigital Library
- S. Smaldone, C. Tonde, V. K. Ananthanarayanan, A. Elgammal, and L. Iftode. The cyber-physical bike: A step towards safer green transportation. In HotMobile '11: Proc. of the 12th Workshop on Mobile Computing Systems and Applications, March 2011. Google ScholarDigital Library
- Z. Sun, G. Bebis, and R. Miller. Monocular precrash vehicle detection: features and classifiers. IEEE Transactions on Image Processing, 15(7):2019--2034, July 2006. Google ScholarDigital Library
- P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1:511, 2001.Google ScholarCross Ref
Index Terms
- WalkSafe: a pedestrian safety app for mobile phone users who walk and talk while crossing roads
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