Advertisement

Cyclist Detection Using Tiny YOLO v2

  • Karattupalayam Chidambaram SaranyaEmail author
  • Arunkumar Thangavelu
  • Ashwin Chidambaram
  • Sharan Arumugam
  • Sushant Govindraj
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)

Abstract

This paper seeks to evaluate the performance of the state of the art object classification algorithms for the purpose of cyclist detection using the Tsinghua–Daimler Cyclist Benchmark. This model focuses on detecting cyclists on the road for its use in development of autonomous road vehicles and advanced driver-assistance systems for hybrid vehicles. The Tiny YOLO v2 algorithm is used here and requires less computational resources and higher real-time performance than the YOLO method, which is extremely desirable for the convenience of such autonomous vehicles. The model has been trained using the training images in the mentioned benchmark and has been tested for the test images available for the same. The average IoU for all the truth objects is calculated and the precision-recall graph for different thresholds was plotted.

Keywords

Tiny Yolo v2 Tsinghua–Daimler Cyclist Benchmark Cyclist detection IoU 

References

  1. 1.
    Li, T., Cao, X., Xu, Y.: An effective crossing cyclist detection on a moving vehicle. In: 2010 8th World Congress on Intelligent Control and Automation, Jinan, pp. 368–372 (2010)Google Scholar
  2. 2.
    Tian, W., Lauer, M.: Fast cyclist detection by cascaded detector and geometric constraint. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, Spain (2015)Google Scholar
  3. 3.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite, CVPR (2012)Google Scholar
  4. 4.
    Yang, F., Choi, W., Lin, Y.: Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)Google Scholar
  5. 5.
    Ren, J., Chen, X., Liu, J., Sun, W., Pang, J., Yan, Q., Tai, Y.W., Xu, L.: Accurate single stage detector using recurrent rolling convolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)Google Scholar
  6. 6.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks (2016). arXiv:1506.01497 [cs.CV]
  7. 7.
    Saleh, K., Hossny, M., Hossny, A., Nahavandi, S.: Cyclist detection in LIDAR scans using faster R-CNN and synthetic depth images. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, pp. 1–6 (2017)Google Scholar
  8. 8.
    Girschick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile (2015)Google Scholar
  9. 9.
    Li, X., Flohr, F., Yang, Y., Xiong, H., Braun, M., Pan, S., Li, K., Gavrila, D.M.: A new benchmark for vision-based cyclist detection. In: 2016 IEEE Intelligent Vehicles Symposium (IV), IEEE (2016)Google Scholar
  10. 10.
    Huang, J., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  11. 11.
    Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger (2016). arXiv:1612.08242 [cs.CV]
  12. 12.
    Shafiee , M.J., Chywl, B., Li, F., Wong, A.: Fast YOLO: a fast you only look once system for real-time embedded object detection in video (2017). arXiv:1709.05943 [cs.CV]
  13. 13.
    Kharchenko, V., Chyrka, I.: Detection of airplanes on the ground using YOLO neural network. In: 2018 IEEE 17th International Conference on Mathematical Methods in Electromagnetic Theory (MMET), Kiev, Ukraine (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Karattupalayam Chidambaram Saranya
    • 1
    Email author
  • Arunkumar Thangavelu
    • 1
  • Ashwin Chidambaram
    • 1
  • Sharan Arumugam
    • 1
  • Sushant Govindraj
    • 1
  1. 1.Vellore Institute of TechnologyVelloreIndia

Personalised recommendations