iosr-JVSP   Volume-1 ~ Issue-4

ABSTRACT:The paper presents a vehicle detection system by locating their headlights and tail lights in the nighttime road environment. The system detects the vehicles light in front of a micro CCD camera assisted vehicle i.e. oncoming & preceding vehicles. Our system automatically controls vehicle's head lights status between low and high beams which avoids the glares for the drivers. The captured frames consist of number of bright objects over dark background. These objects are due to vehicle lamps, road reflection etc. The captured object features are used to train and classify the two classes of lights in vehicles light & other light source. The machine learning based approach, Support Vector Machine (SVM) is used to accomplish this task. The output of the SVM is simply the signed distance of the test instance from the separating hyperplane. The result show the SVM is effective to classify number of lights and it is useful for vehicle validation.
Keywords:Computer vision, Driver Assistance, Image processing, Support Vector Machine, Vehicle detection.

[1] Chun-Che Wang, Shih-Shinh Huang , Li-Chen Fu, Pei-Yun Hsiao, National Taiwan University, Taipei, Taiwan, R.O.C., Driver
Assistance System for Lane Detection and Vehicle Recognition with Night Vision, IEEE Transactions on Intelligent Transportation
Systems, Vol. 3, No. 3, pp 203-209, Sept. 2002.
[2] Ming-Yang Chem , Ping-Cheng Hou, National Chung Cheng University, Min-Hsiung, Chia-Yi, Taiwan, The Lane Recognition and
Vehicle Detection at Night for A Camera-Assisted Car on Highway, Proceedings of the 1003 IEEE, International Conference on
Robotics & Automation, Taipei. Taiwan, September 14-19, 2003.
[3] Yi-Ming Chan, Shih-Shinh Huang, Member, IEEE, Li-Chen Fu, Fellow, IEEE, and Pei Yung Hsiao, Member, IEEE, Vehicle
Detection Under Various Lighting Conditions by Incorporating Particle Filter, Proceedings of the 2007 IEEE Intelligent
Transportation Systems Conference Seattle, WA, USA, Sept. 30 - Oct. 3, 2007.
[4] O'malley R. Glavin, M. and Jones. Connaught Automotive Research Group Department of Electronic Engineering, National
University of Ireland, Galway, Vehicle Detection at Night Based on Tail-Light Detection, ISVCS. 1st International ICST
Symposium on Vehicular Computing Systems. ISVCS2008.3546, E. 2008.
[5] P. F. Alcantarilla, L.M. Bergasa, P. Jim´enez, M. A. Sotelo, I. Parra, D. Fernandez, Department of Electronics. University of
Alcal´a, Alcal´a de Henares (Madrid), Spain, Night Time Vehicle Detection for Driving Assistance LightBeam Controller, 2008
IEEE Transaction, Eindhoven University of Technology Eindhoven, The Netherlands, June 4 -6, 2008.
[6] Peachanika Thammakaroon, Poj Tangamchit Department of Control System and Instrumentation Engineering, Kmg Mongkut's
University of Technology Thonburi, Thailand, Predictive Brake Warning at Night using Taillight Characteristic, IEEE International
Symposium on Industrial Electronics (ISlE 2009).
[7] Steffen Gormer, Dennis Muller, Stephanie Hold, Faculty of Electrical Engineering and Media Technologies, University of
Wuppertal, D-42119 Wuppertal, Germany. Mirko Meuter, and Anton Kummert, Delphi Electronics & Safety Advanced
Engineering D-42119 Wuppertal, Germany. Vehicle Recognition and TTC Estimation at Night based on Spotlight Pairing,
Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA, October 3 -7, 2009.
[8] Andrea Fossati, CV Lab – EPFL 1015 Lausanne-Switzerland, Patrick Schonmann Cinetis SA 1920 Martigny – Switzerland, Pascal
Fua CV Lab – EPFL 1015 Lausanne- Switzerland, Real-Time Vehicle Tracking for Driving Assistance, Machine Vision and
Applications (ISSN: 1432-1769), 24 Jun 2010.
[9] Ronan O'Malley, Edward Jones, Member, IEEE, and Martin Glavin, Member, IEEE, Rear-Lamp Vehicle Detection and Tracking in
Low-Exposure Color Video for Night Conditions, IEEE Transactions on intelligent transportation systems, vol. 11, no. 2, June 2010.
[10] M. Betke, E. Haritaoglu, and L. S. Davis, Real-time multiple vehicle detection and tracking from a moving vehicle, Mach. Vis.
Appl., Vol. 12, 2000, pp. 69-83.

ABSTRACT:Super-resolution aims to produce a high-resolution image from a set alone or more low-resolution images by recovering or inventing plausible high-frequency image content. Typical approach is try to reconstruct a high-resolution image using the sub-pixel displacements of several low-resolution images, usually regularized by a generic smoothness prior over the high-resolution image space. Throughout this paper, a higher resolution image is defined as an image with more resolving power. Super Resolution consists of two main steps: image registration and image reconstruction. Precise alignment of the input images is one of the important terms. In this paper, we have implemented and tested motion estimation algorithms and image reconstruction algorithms in spatial domain as well as in frequency domain in order to study their analytical parameters and a high resolution image is created by using bicubic interpolation over the prealiased images. Also the experimental as well as analytical results of this paper are successful in spatial domain as per the application of image superresolution.
Keywords:Superresolution, Image registration, Image Reconstruction.

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ABSTRACT: Convolution is fundamental operation of most of the signal processing systems. It is necessity of time to speed up convolution process at very appreciable extent. Here Direct method of computing the discrete linear convolution of finite length sequences is used. The approach is easy to learn because of the similarities to computing the multiplication of two numbers by a pencil and paper calculation. Multipliers are basic building blocks of convolver. Since it dominates most of the execution time, for optimizing the speed, 4×4 bit Vedic multipliers based on Urdhva Tiryagbhyam sutra are used. Convolver has delay of 17.996 ns when implemented on 90 nm process technology FPGA. It also provides necessary modularity, expandability, and regularity to form different convolutions for any number of bits. The coding is done in VHDL (Very High Speed Integrated Circuits Hardware Description Language) for the FPGA , as it is being increasingly used for variety of computationally intensive applications. Simulation and synthesis is done using Xilinx 9.2i.
Keywords: Convolution , FPGA ,Vedic Mathematics

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Society Annual Symposium on VLSI ,p.323 ,July 2011.
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education, VOL.39, NO. 1, 1996.
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Mathematics",IEEE,2008 International Soc Design Conference.
[4] Sumit Vaidya, Deepak Dandekar ,"Delay power performance comparison of multipliers in VLSI circuit design", International Journal
of Computer Networks & Communications, Vol 2, No. 4,July 2010.
[5] Chao Cheng , Keshab K. Parhi "Hardware Efficient Fast Parallel FIR Filter Structures Based on Iterated Short Convolution" IEEE,
and, IEEE transaction on circuits and systems, VOL. 51, NO. 8, 2004.
[6] Thomas Oelsner ,"Implementation of Data Convolution Algorithms in FPGAs" http://www.quicklogic.com/images/appnote18.pdf
[7] Abraham H. Diaz, Domingo Rodriguez ,"One Dimentional Cyclic Convolution Algorithms With Minimal Multiplicative Complexity", ICASSP.
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Transform", Proceedings of the world Congress on Engineering Vol 1,July 2009.
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