Skip to main content

Fully Annotated Indian Traffic Signs Database for Recognition

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1154))

Abstract

We present a fully annotated database of Indian traffic signs for classification with nearly 1700 images. The images have been taken in varied weather conditions in daylight. The images are of varied parameters and reflect strong variations in terms of occlusion, illumination, skew, distance and other conditions. Semi-automated annotation makes the ground truth reliable. This is the first such attempt to make an Indian database to the best of our knowledge.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

  1. Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: IJCNN, vol. 6, p. 7 (2011)

    Google Scholar 

  2. Mahajan, R.: Emotion recognition via EEG using neural network classifier. In: Soft Computing: Theories and Applications, pp. 429–438. Springer, Singapore (2018)

    Google Scholar 

  3. Srivastava, M., Saini, S., Thakur, A.: Analysis and parameter estimation of microstrip circular patch antennas using artificial neural networks. In: Soft Computing: Theories and Applications, pp. 285–292. Springer, Singapore (2018)

    Google Scholar 

  4. Giri, J.P., Giri, P.J., Chadge, R.: Neural network-based prediction of productivity parameters. In: Soft Computing: Theories and Applications, pp. 83–95. Springer, Singapore (2018)

    Google Scholar 

  5. Timofte, R., Gool, L.V.: Sparse representation based projections. In: British Machine Vision Conference (BMVC, UK) (2011)

    Google Scholar 

  6. Mogelmose, A., Trivedi, M.M., Moeslund, T.B.: Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans. Intell. Transp. Syst. 13(4), 1484–1497 (2012)

    Article  Google Scholar 

  7. University of Zagreb Faculty of Electrical Engineering and Computing. http://www.zemris.fer.hr/~ssegvic/mastif/datasets.shtml

  8. University of Zagreb Faculty of Electrical Engineering and Computing. http://www.zemris.fer.hr/~kalfa/Datasets/rMASTIF/

  9. Department of Computer Engineering, Automatic and Management, Sapienza University. http://users.diag.uniroma1.it/bloisi/ds/dits.html

  10. National Laboratory of Pattern Recognition. http://www.nlpr.ia.ac.cn/pal/trafficdata/recognition.html

  11. University of California San Diego. https://www.cvl.isy.liu.se/research/datasets/traffic-signs-dataset/download/

  12. Adobe.https://www.adobe.com/in/products/photoshop-lightroom.html

  13. Visual Geometry Group Department of Engineering Science, University of Oxford. http://www.robots.ox.ac.uk/~vgg/software/via/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Banhi Sanyal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sanyal, B., Mohapatra, R.K., Dash, R. (2020). Fully Annotated Indian Traffic Signs Database for Recognition. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_63

Download citation

Publish with us

Policies and ethics