Open Access Open Access  Restricted Access Subscription or Fee Access

An Electronic Eye for Visually Impaired (Nanotechnology)

M. Nivedha, S. Priyanka


This paper proposes a method for detecting frontal pedestrian crossings from image data obtained with a single camera as a travel aid for the visually challenged. This would be embedded on a pair of glasses, will be capable to detect the existence and location of a zebra crossing, to measure the wide of the road, and to detect the color of the traffic lights. The process of detecting a cross road is a done before followed by the process for detecting the state of the traffic lights (colors). It is important for the visually challenged (blind) to know whether or not a frontal area is meant for crossing. The existence of a cross road is detected in two steps. In the first step, edge of road is detected and pattern is detected are employed to identify the crossroad. In the second step, the existence of a crossroad is detected by checking the periodicity of white lines on the road using projective invariants technique. Then the traffic light detector is used to check the pedestrian light and the time in the display panel. The calculated time is then compared with the average time needed for a blind person to cross. The observations are conveyed through voice signals using the voice- vision technology. Thus, this effective technology aids vocational training for the visually impaired throughout the globe.


Traffic Detection, Crossroad Pattern, Sobel Gradient, Edge Detection, Timing Unit.

Full Text:



Ratner(Mark);Ratner(Daniel);-Nanotechnology-The Gentle Introduction to the next Big Idea.

D. Koller , J. Weber , T. Huang , J. Malik , J. Ogasawara , G. Rao and S. Russell "Towards robust automatic traffic scene analysis", .

M. Vargas, J. M. Milla , S. L. Toral and F. Barrero "An enhanced background estimation algorithm for vehicle detection in urban traffic scenes".

J. Zhou, D. Gao and D. Zhang “Moving vehicle detection for automatic traffic monitoring".

J. M. A. Alvarez and A. M. Lopez “Road detection based upon illuminanance invariance", IEEE Trans. Intell. Transp. Syst .

W. Zhang, Q. M. J. Wu and X. Yang “Multilevel framework to detect and handle vehicles", IEEE Trans.

N. K. Kanhere and S. T. Birchfield “Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features”.

L. Breiman and J. Friedman they are Estimating optimal transformations for multiple regression and correlation.

X. Chen. Coordinate-independent sparse sufficient dimension reduction and variable selection. Ann. Stat.

R. Dennis Cook. Principal fitted components for dimension reduction in regression process. Statistical Science.

J. Fan Local Polynomial Modelling and its Applications at Chapman.

J. Fan and R. Li. Variable selection via nonconcave penalized likelihood and its oracle properties.Stat. Assoc., 2001.

M. Wang, F. Sha, and M. Jordan. Unsupervised kernel based dimension reduction. NIPS 23.2010.

D., Jurafsky, ―Speech Recognition and Synthesis: Acoustic Modeling‖, winter 2005.

S.K., Podder, ―Segment-based Stochastic Modelings for Speech Recognition at Department of Electrical and Electronic Engineering, Matsuyama , Japan, 1997.

S.M., Ahadi, H., Sheikhzadeh, R.L., Brennan, and G.H., Freeman, ―An Efficient Front-End for Automatic Speech Recognition. IEEE International Conference on Electronics, Circuits and Systems (ICECS2003), Sharjah.

M., Jackson, ―Automatic Speech Recognition: Human Computer Interface for Kinyarwanda Language.Master and Faculty of Information Technology, Makerere University, 2005.

M.R., Hasan, M., Jamil, and M.G., Saifur Rahman, ―Speaker Identification Using Mel Frequency Cepstral Coefficients. 3rd International Conference on Electrical and Computer Engineering.

M.Z., Bhotto and M.R., Amin, ―Bangali Text Dependent Speaker Identification Using MelFrequency Cepstrum Coefficient and VectorQuantization‖. 3rd International Conference on Electrical and Computer Engineering in the Bangladesh.

Sobotottka,Looking for Faces and Facial Feature in Color Images and in the Pattern Recognition and Image Analysis,.

Kenneth R.Castleman. Digital Image Processing. Beijing: Tsinghua university.

Ostu N.A. Threshold Selection Method from Gray - Level Histograms [J].IEEE Trans on Systems. Man and Cybernetics, SMC.

S. Price, "Edges: The Canny Edge Detector", 1996.

E. Jernigan, "Color Edge Detection using RGB coloursUsing Jointly Euclidean Distance and Vector Angle".

S. Baluja and T. Kanade, “Neural Network in the Based Face Detection,” in IEEE on Pattern Analysis and Machine Intelligence.

Hazem M. El-Bakry, “Fast Object/Face Detection Using Neural Networks and Fast FourierTransform,”in the International Journal using the Signal Processing.

Betke, M., Makris, N. C., 1995. Fast object recognition in noisy images using simulated annealing. In: Proceedings of the 5th Internationa l Conference based on Computer Vision in cambridge university .Bonmassar, G., Schwartz, E. L., 1998. Improved cross-correlation for template matching on the Laplacian pyramid based on Pattern Recognition Letters

Penz, H, W, Mayer, K., 2001. Fast real-time recognition and quality inspection of printed characters via point-correlation.

J. Wu, A. Chung, Cross entropy: a new solver for Markov random field modeling and applications to medical image segmentation, in: The International Conference on Computer-Assisted Intervention (MICCAI'05), 2005. , , Standard NIM Instrumentation System. U.S. NIM Committee, U.S. Department of Energy Report, DOE, 1990.

Description of the JRA1 Trigger Logic Unit (TLU). D. Cussans, EUDET 2007.

The Design of a Flexible Global Calorimeter Trigger System for the Compact Muon Solenoid Experiment, J. J.Brooke, D. G. Cussans , R. J. E. Frazier, S. B. Galagedera,G. P. Heath, B. J. Huckvale, S. J. Nash, D. M. Newbold, A.A. Shah. Manuscript submitted for publication in Journal of the field Instrumentation.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.