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Real-time vehicle detection using equi-height mosaicking image

Published: 01 October 2013 Publication History

Abstract

In this paper, we present a real-time forward vehicle detection warning system using a novel image representation called an equi-height mosaicking image. The proposed system uses a GPU (graphic processing unit) based approach for the real-time processing of a road scene image captured from a single camera. The equi-height mosaicking image improves the execution time of the existing GPU-based acceleration approach without decreasing the detection accuracy. The equi-height image is generated as follows. After a geometric analysis of a road scene using the vanishing point and horizon, we crop a set of image strips by sampling several positions on the road at uniform intervals. The height of each image strip is computed by projecting the predefined height of a vehicle at a distant position onto an image plane. After all the cropped images are resized to the uniform height required to build the equi-height image, we concatenate these resized images, similar to a panorama image, to create the equi-height mosaicking image. The concatenated image has a long width but the height of the image is uniform. The proposed system then performs a GPU-based vehicle detection on the concatenated image using a 1D search based support vector machine (SVM) classification. The proposed method is faster than the GPU-based OpenCV HOG detector because of the reduced search area.

References

[1]
Nissan around view monitor. http://www.nissan-global.com/EN/TECHNOLOGY/OVERVIEW/avm.html/.
[2]
Opencv gpu hog detector. http://docs.opencv.org/modules/gpu/doc/object_detection.html/.
[3]
Short wave infrared night vision camera systems for vehicle navigation. http://www.sensorsinc.com/nightvision.html.
[4]
W. Burger and M. J. Burge. Digital image processing. Springer, 2008.
[5]
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886--893. IEEE, 2005.
[6]
F. Devernay. A Non-Maxima Suppression Method for Edge Detection with Sub-Pixel Accuracy. Technical Report RR-2724, INRIA, Nov. 1995.
[7]
F. Han, Y. Shan, R. Cekander, H. S. Sawhney, and R. Kumar. A two-stage approach to people and vehicle detection with hog-based svm. In Performance Metrics for Intelligent Systems 2006 Workshop, pages 133--140, 2006.
[8]
R. Hartley and A. Zisserman. Multiple view geometry in computer vision, volume 2. Cambridge Univ Press, 2000.
[9]
L. M. Manevitz and M. Yousef. One-class svms for document classification. J. Mach. Learn. Res., 2:139--154, Mar. 2002.
[10]
M. Nieto and L. Salgado. Real-time robust estimation of vanishing points through nonlinear optimization. pages 772402-772402--14, 2010.
[11]
M. W. Park, K. H. Jang, and S. K. Jung. Panoramic vision system to eliminate driver's blind spots using a laser sensor and cameras. International Journal of Intelligent Transportation Systems Research, 10(3):101--114, 2012.
[12]
V. Prisacariu and I. Reid. fastHOG - a real-time GPU implementation of HOG. Technical Report 2310/09, Department of Engineering Science, Oxford University, 2009.
[13]
I. Safonova, H. Leeb, S. Kimb, and D. Choib. Intellectual two-sided card copy.
[14]
G. P. Stein, O. Mano, and A. Shashua. Vision-based acc with a single camera: bounds on range and range rate accuracy. In Intelligent vehicles symposium, 2003. Proceedings. IEEE, pages 120--125. IEEE, 2003.
[15]
Z. Sun, G. Bebis, and R. Miller. On-road vehicle detection using evolutionary gabor filter optimization. Intelligent Transportation Systems, IEEE Transactions on, 6(2):125--137, 2005.
[16]
Z. Sun, G. Bebis, and R. Miller. On-road vehicle detection: A review. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(5):694--711, 2006.
[17]
P. H. S. Torr and A. Zisserman. Mlesac: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78:2000, 2000.
[18]
X. Wang and B. E. Shi. Gpu implemention of fast gabor filters. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pages 373--376. IEEE, 2010.
[19]
Z. Zhang. A exible new technique for camera calibration. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(11):1330--1334, 2000.

Cited By

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  • (2018)A Study on Real-Time Detection Method of Lane and Vehicle for Lane Change Assistant System Using Vision System on HighwayEngineering Science and Technology, an International Journal10.1016/j.jestch.2018.06.00621:5(822-833)Online publication date: Oct-2018
  • (2015)TLD based vehicle tracking system for AR-HUD using HOG and online SVM in EHMI2015 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE.2015.7066418(289-290)Online publication date: Jan-2015
  • (2014)Pedestrian detection of road scenes using depth and intensity featuresProceedings of the 2014 Conference on Research in Adaptive and Convergent Systems10.1145/2663761.2664226(144-148)Online publication date: 5-Oct-2014
  • Show More Cited By

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cover image ACM Conferences
RACS '13: Proceedings of the 2013 Research in Adaptive and Convergent Systems
October 2013
529 pages
ISBN:9781450323482
DOI:10.1145/2513228
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 October 2013

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Author Tags

  1. GPU
  2. SVM
  3. equi-height
  4. mosaicking image
  5. real-time
  6. system
  7. vehicle detection

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RACS'13
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RACS'13: Research in Adaptive and Convergent Systems
October 1 - 4, 2013
Quebec, Montreal, Canada

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RACS '13 Paper Acceptance Rate 73 of 317 submissions, 23%;
Overall Acceptance Rate 393 of 1,581 submissions, 25%

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Cited By

View all
  • (2018)A Study on Real-Time Detection Method of Lane and Vehicle for Lane Change Assistant System Using Vision System on HighwayEngineering Science and Technology, an International Journal10.1016/j.jestch.2018.06.00621:5(822-833)Online publication date: Oct-2018
  • (2015)TLD based vehicle tracking system for AR-HUD using HOG and online SVM in EHMI2015 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE.2015.7066418(289-290)Online publication date: Jan-2015
  • (2014)Pedestrian detection of road scenes using depth and intensity featuresProceedings of the 2014 Conference on Research in Adaptive and Convergent Systems10.1145/2663761.2664226(144-148)Online publication date: 5-Oct-2014
  • (2014)Development of augmented forward collision warning system for Head-Up Display17th International IEEE Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2014.6958054(2277-2279)Online publication date: Oct-2014

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