Skip to main content

Novel Real-Time Automobile Detection Algorithm for Blind Spot Area

  • Conference paper
  • First Online:
Frontier and Innovation in Future Computing and Communications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 301))

  • 2165 Accesses

Abstract

Many facets of automobile technology are moving to information technology (IT)-based convergence systems. Sensors and equipment used to prevent accidents that occur due to drive negligence are under development. An automobile detection algorithm for blind spot areas while driving is proposed. Detection of candidate vehicles is based on vehicle features, and then a vehicle is subject to verification of the detected candidate vehicle area. Using a Haar-like feature for vehicle detection, an adaptive vehicle verification process based on brightness values of vehicle areas is used. The hierarchical scheme of the proposed algorithm is efficient and accurate, based on detection and verification results in the blind spot area.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. http://www.internationaltransportforum.org/Pub/pdf/13KeyStat2012.pdf

  2. Trivedi MM, Cheng SY (2007) Holistic sensing and active displays for intelligent driver support systems. IEEE Comput 40(5):60–68

    Article  Google Scholar 

  3. Sun Z, Bebis G, Miller R (2006) On-road vehicle detection: a review. IEEE Trans Pattern Anal Mach Intell 28(5):694–711

    Article  Google Scholar 

  4. Alessandretti G, Broggi A, Cerri P (2001) Vehicle and guard rail detection using radar and vision data fusion. IEEE Trans on Intell Transp Syst 8:95–105

    Article  Google Scholar 

  5. Trivedi MM, Cheng S (2007) Holistic sensing and active displays for intelligent driver support systems. Computer 40(5):60–68

    Article  Google Scholar 

  6. Wu Y-J et al (2007) Image processing techniques for lane-related information extraction and multi-vehicle detection in intelligent highway vehicles. Int J Automot Technol 8(4):513–520

    Google Scholar 

  7. Roth PM, Bischof H (2008) Active sampling via tracking In: Proceedings of IEEE conference on computer vision pattern recognition, pp 1–8

    Google Scholar 

  8. Vijayanarasimhan S, Grauman K (2008) Multi-level active prediction of useful image annotations for recognition, In: Proceedings of neural information processing system conference, pp 1705–1712

    Google Scholar 

  9. Enzweiler M, Gavrila DM (2008) A mixed generative-discriminative framework for pedestrian classification. In: Proceedings of IEEE conference on computer vision pattern recognition, pp 1–8

    Google Scholar 

  10. Sun Z, Bebis G, Miller R (2006) Monocular precrash vehicle detection: features and classifiers. Proceedings of IEEE Transactions on Image 15(7):2019–2034

    Article  Google Scholar 

  11. Balcones D, et al (2009). Real-time vision-based vehicle detection for rear-end collision mitigation systems. Comput Aided Syst Theory-EUROCAST 2009. Springer, Heidelberg, pp 320–325

    Google Scholar 

  12. Alonso D, Salgado L, Nieto M (2007). Robust vehicle detection through multidimensional classification for on board video based systems In: IEEE international conference on image processing ICIP 2007, vol 4

    Google Scholar 

  13. Song GY, Lee KY, Lee JW, (2008) Vehicle detection by edge-based candidate generation and appearance-based classification. In: IEEE intelligent vehicles symposium

    Google Scholar 

  14. Han S, Han Y, Hahn H (2009) Vehicle detection method using Haar-like feature on real time system. World Acad Sci Eng Techonol 59

    Google Scholar 

  15. Viola P, Jones M (2001), Rapid object detection using a boosted cascade of simple features. In: Processing IEEE conference computer vision pattern recognition vol 1, pp 511–518

    Google Scholar 

  16. Lin CC, et al (2007) Development of a multimedia-based vehicle lane departure warning, forward collision warning and event video recorder systems. In: Multimedia workshops. ISMW’07. 9th IEEE international symposium on IEEE

    Google Scholar 

  17. Kapoor A, Grauman K, Urtasun R, Darrell T (2007) Active learning with Gaussian processes for object categorization, In: Proceedings of IEEE international conference on computer vision, pp 1–8

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Byung-Gyu Kim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this paper

Cite this paper

Yang, SH., Hong, GS., Ryong, B., Kim, BG. (2014). Novel Real-Time Automobile Detection Algorithm for Blind Spot Area. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_91

Download citation

  • DOI: https://doi.org/10.1007/978-94-017-8798-7_91

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-8797-0

  • Online ISBN: 978-94-017-8798-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics