On-road vehicle detection in varying weather conditions using faster R-CNN with several region proposal networks | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

On-road vehicle detection in varying weather conditions using faster R-CNN with several region proposal networks

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Developing automated systems to detect and track on-road vehicles is a demanding research area in Intelligent Transportation System (ITS). This article proposes a method for on-road vehicle detection and tracking in varying weather conditions using several region proposal networks (RPNs) of Faster R-CNN. The use of several RPNs in Faster R-CNN is still unexplored in this area of research. The conventional Faster R-CNN produces regions-of-interest (ROIs) through a single fixed sized RPN and therefore cannot detect varying sized vehicles, whereas the present investigation proposes an end-to-end method of on-road vehicle detection where ROIs are generated using several varying sized RPNs and therefore it is able to detect varying sized vehicles. The novelty of the proposed method lies in proposing several varying sized RPNs in conventional Faster R-CNN. The vehicles have been detected in varying weather conditions. Three different public datasets, namely DAWN, CDNet 2014, and LISA datasets have been used to evaluate the performance of the proposed system and it has provided 89.48%, 91.20%, and 95.16% average precision on DAWN, CDNet 2014, and LISA datasets respectively. The proposed system outperforms the existing methods in this regard.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bertozzi M, Broggi A, Castelluccio S (1997) A real-time oriented system for vehicle detection. J Syst Archit 43(1-5):317–325

    Article  Google Scholar 

  2. Cai Z, Vasconcelos N (2018) Cascade R-CNN: Delving into high quality object detection, proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, USA, pp. 6154–6162

  3. Cai W, Wei Z (2020) Remote Sensing Image Classification Based on a Cross-Attention Mechanism and Graph Convolution, IEEE Geosci Remote Sens Lett, pp. 1–5

  4. Chabot F, Chaouch M, Rabarisoa J, Teuliere C, Chateau T (2017) Deep MANTA: A Coarse-to-Fine many-task network for joint 2D and 3D vehicle analysis from monocular image, Proceedings of the IEEE conference on computer vision and pattern recognition, Hawaii, USA, pp. 2040–2049

  5. Chan Y, Huang S, Fu L, Hsiao P (2007) Vehicle detection under various lighting conditions by incorporating particle filter, proceedings of the IEEE intelligent transportation systems conference, Seattle, USA, pp. 534–539

  6. Chellappa R, Qian G, Zheng Q (2004) Vehicle detection and tracking using acoustic and video sensors, proceedings of the IEEE international conference on acoustics, speech, and signal processing, Montreal, Canada, pp. 793–796

  7. Chen D, Chen G, Wang Y (2013) Real-time dynamic vehicle detection on resource-limited mobile platform. IET Comput Vis 7(2):81–89

    Article  MathSciNet  Google Scholar 

  8. Cheon M, Lee W, Yoon C, Park M (2012) Vision-based vehicle detection system with consideration of the detecting location. IEEE Trans Intell Transp Syst 13(3):1243–1252

    Article  Google Scholar 

  9. Dong E, Deng M, Tong J, Jia C, Du S (2019) Moving vehicle tracking based on improved tracking-learning-detection algorithm. IET Comput Vis 13(8):730–741

    Article  Google Scholar 

  10. Fossati A, Schnmann P, Fua P (2011) Real-time vehicle tracking for driving assistance. Mach Vis Appl 22:439–448

    Article  Google Scholar 

  11. Ghosh R, Thakre S, Kumar P (2018) A vehicle number plate recognition system using region-of-interest based filtering method, proceedings of the 2018 conference on information and communication technology. Jabalpur, pp 1–6

  12. Hadi RA, George LE, Mohammed MJ (2017) A computationally economic novel approach for real-time moving multi-vehicle detection and tracking toward efficient traffic surveillance. Arab J Sci Eng 42:817–831

    Article  Google Scholar 

  13. Haselhoff A, Kummert A (2009) A vehicle detection system based on Haar and triangle features. Proceedings of the IEEE Intelligent Vehicles Symposium, Xi'an, China, pp 261–266

  14. Haselhoff A, Kummert A (2009) An evolutionary optimized vehicle tracker in collaboration with a detection system. Proceedings of the IEEE Intelligent Transportation Systems Conference, St. Louis, USA, pp 1–6

  15. Hassaballah M, Kenk MA, Henawy IME (2020) Local binary pattern‑based on‑road vehicle detection in urban traffic scene. Pattern Anal Applic 23:1505–1521

    Article  Google Scholar 

  16. Hassaballah M, Kenk MA, Muhammad K, Minaee S (2020) Vehicle Detection and Tracking in Adverse Weather Using a Deep Learning Framework, IEEE Trans Intell Transport Syst, pp. 1–13

  17. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN, Proceedings of the IEEE conference on computer vision and pattern recognition, Hawaii, USA, pp. 2961–2969

  18. Hu X, Xu X, Xiao Y, Chen H, He S, Qin J, Heng PA (2019) SINet: a scale-insensitive convolutional neural network for fast vehicle detection. IEEE Trans Intell Transp Syst 20(3):1010–1019

    Article  Google Scholar 

  19. Kenk MA, Hassaballah M (2020) DAWN: Vehicle Detection in Adverse Weather Nature Dataset, arXiv preprint arXiv:2008.05402

  20. Khairdoost N, Monadjemi SA, Jamshidi K (2013) Front and rear vehicle detection using hypothesis generation and verification. Sig Image Process 4(4):31–50

    Google Scholar 

  21. Li Y, Chen Y, Wang N, Zhang Z (2019) Scale-aware trident networks for object detection, arXiv:1901.01892. [Online]. Available: http://arxiv.org/abs/1901.01892. Accessed 18 Dec 2020.

  22. Lin TY, Goyal P, Girshick R, He K, Dollar P (2017) Focal loss for dense object detection, Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp. 2980–2988

  23. Matthews N, An P, Charnley D, Harris C (1996) Vehicle detection and recognition in greyscale imagery, control engineering practice, volume 4. Issues 4:473–479

    Google Scholar 

  24. Ming Q, Jo KH (2011) Vehicle detection using tail light segmentation, proceedings of the 6th international forum on strategic technology, Harbin, China, pp. 729–732

  25. Mohamed A, Issam A, Mohamed B, Abdellatif B (2015) Real-time detection of vehicles using the Haar-like features and artificial neuron networks. Procedia Comput Sci 73:24–31

    Article  Google Scholar 

  26. Redmon J, Farhadi A (2018) YOLOv3: An incremental improvement, arXiv:1804.02767. [Online]. Available: http://arxiv.org/abs/1804.02767. Accessed 18 Dec 2020.

  27. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Proceedings of the Neural Information Processing Systems, Montreal, Canada, pp 91–99

  28. Singh V, Srivastava A, Kumar S, Ghosh R (2019) A Structural Feature Based Automatic Vehicle Classification System at Toll Plaza, proceedings of the 4th international conference on internet of things and connected technologies. Jaipur, pp 1–10

  29. Sivaraman S, Trivedi M (2010) A general active-learning framework for on-road vehicle recognition and tracking. IEEE Trans Intell Transp Syst 11(2):267–276

    Article  Google Scholar 

  30. Sivaraman S, Trivedi MM (2014) Active learning for on-road vehicle detection: a comparative study. Mach Vis Appl 25(3):599–611

    Article  Google Scholar 

  31. Sun Z, Bebis G, Miller R (2006) Monocular precrash vehicle detection: features and classifiers. IEEE Trans Image Process 15(7):2019–2034

    Article  Google Scholar 

  32. Tian Y, Dong H, Jia L, Li S (2014) A vehicle re-identification algorithm based on multi-sensor correlation. J Zhejiang Uni Sci C 15(5):372–382

    Article  Google Scholar 

  33. Wang Y, Jodoin PM, Porikli F, Konrad J, Benezeth Y, Ishwar P (2014) CDnet 2014:an expanded change detection benchmark dataset, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 387–394

  34. Wen X, Shao L, Fang W, Xue Y (2015) Efficient feature selection and classification for vehicle detection. IEEE Trans Circuits Syst Video Technol 25(3):508–517

    Article  Google Scholar 

  35. Xiang Y, Choi W, Lin Y, Savarese S (2017) Subcategory-aware convolutional neural networks for object proposals and detection, Proceedings of the IEEE winter conference on applications of computer vision, Santa Rosa, USA, pp. 924–933

  36. Yan G, Yu M, Yu Y, Fan L (2016) Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification. Int J Light Electron Opt 127(19):7941–7951

    Article  Google Scholar 

  37. You H, Tian S, Yu L, Lv Y (2020) Pixel-level remote sensing image recognition based on bidirectional word vectors. IEEE Trans Geosci Remote Sens 58(2):1281–1293

    Article  Google Scholar 

  38. Yu T, Shin H (2015) Detecting partially occluded vehicles with geometric and likelihood reasoning. IET Comput Vis 9(2):174–183

    Article  MathSciNet  Google Scholar 

  39. Zhou Y, Liu L, Shao L, Mellor M (2016) DAVE: a unified framework for fast vehicle detection and annotation, proceedings of the European conference on computer vision, Amsterdam, Netherlands, pp. 278–293

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India

    Rajib Ghosh

Authors
  1. Rajib Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajib Ghosh.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghosh, R. On-road vehicle detection in varying weather conditions using faster R-CNN with several region proposal networks. Multimed Tools Appl 80, 25985–25999 (2021). https://doi.org/10.1007/s11042-021-10954-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10954-5

Keywords

Search

Navigation