Advertisement

Pedestrian Intention Detection Using Faster RCNN and SSD

  • Debapriyo Roy ChowdhuryEmail author
  • Priya Garg
  • Vidya N. More
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1046)

Abstract

In the domain of Intelligent Monitoring, Smart Driving and Robotics, Pedestrian intention detection is a prime discipline of object recognition. Currently, several pedestrian detection techniques are proposed however, just a handful are re-ported in the domain of pedestrian ‘intention’ detection. Due to the complications of the image background and pedestrian posture diversity, pedestrian intention detection is still a challenge which requires concise algorithms. In this paper, Single Shot Detector (SSD) is compared with Faster Region Convolutional Neural Network (Faster RCNN) architecture of deep neural network by applying different Convolutional Neural Network (CNN) models. Experiments have been conducted in a wide spectrum to obtain various models of Faster RCNN and SSD through compatible alterations in algorithm and parameters tuning. In this paper, Faster R-CNN and SSD architecture have been trained and their results are compared. New and simple evaluation performance parameters are suggested namely: Percentage Detection Index, Percentage Recognition Index and Precision score as compared to the traditional mean average precision (mAp) found in literature. While training these architectures with 1350 images, Faster RCNN learned three times faster than SSD with 2% increased accuracy.

Keywords

Pedestrian intention detection Faster R-CNN Pre-trained CNN SSD Tensorflow 

References

  1. 1.
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893, IEEE Computer Society (2005)Google Scholar
  3. 3.
    Chen, K., Song, X., Zhai, X., Zhang, B., Hou, B., Wang, Y.: An integrated deep learning framework for occluded pedestrian tracking. IEEE Access (2019)Google Scholar
  4. 4.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  5. 5.
    Wanguo, W.: Research on RCNN based image recognition of unmanned aerial vehicle inspection power components. J. Earth Inf. Sci. 19(2), 256–263 (2017)Google Scholar
  6. 6.
    Girshick, R.: Fast R-CNN. In: Computer Science (2015)Google Scholar
  7. 7.
    Ren, S.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  8. 8.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10590-1_53CrossRefGoogle Scholar
  9. 9.
    Sangineto, E., Nabi, M., Culibrk, D., Sebe, N.: Self paced deep learning for weakly supervised object detection. IEEE Trans. Pattern Anal. Mach. Intell. 41(3), 712–725 (2019)CrossRefGoogle Scholar
  10. 10.
    Chengdu, H., Wenguang, H., Bin, Y.: Video pedestrian detection based on codebook background modeling in video. Transducer Microsyst. Technol. 36(3), 144–146 (2017)Google Scholar
  11. 11.
    Chunfeng, Z., Jiatao, S., Wanliang, W.: A survey of pedestrian detection technology. Telev. Technol. 38(3), 157–162 (2014)Google Scholar
  12. 12.
    Salahat, E., Qasaimeh, M.: Recent advances in features extraction and description algorithms: a comprehensive survey. In: IEEE International Conference on Industrial Technology (ICIT), pp. 1059–1063. IEEE (2017)Google Scholar
  13. 13.
    Baroffio, L., Redondi, A.E.C., Tagliasacchi, M., Tubaro, S.: A survey on compact features for visual content analysis. APSIPA Trans. Signal Inf. Process. 5 (2016)Google Scholar
  14. 14.
    Yang, H., Wen, J., Wu, X.-J., He, L., Mumtaz, S.G.: An efficient edge artificial intelligence multi-pedestrian tracking method with rank constraint. IEEE Trans. Ind. Inform. (2019)Google Scholar
  15. 15.
    Ullah, A., Xie, H., Farooq, M.O., Sun, Z.: Pedestrian detection in infrared images using fast RCNN. In: Eighth IEEE International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6 (2018)Google Scholar
  16. 16.
    Manana, M., Tu, C., Owolawi, P.A.: Preprocessed faster RCNN for vehicle detection. In: IEEE International Conference on Intelligent and Innovative Computing Applications (ICONIC), pp. 1–4 (2018)Google Scholar
  17. 17.
    Ning, C., Zhou, H., Song, Y., Tang, J.: Inception single shot multibox detector for object detection. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 549–554 (2017)Google Scholar
  18. 18.
  19. 19.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Debapriyo Roy Chowdhury
    • 1
    Email author
  • Priya Garg
    • 1
  • Vidya N. More
    • 1
  1. 1.College of Engineering, PuneSPPU UniversityPuneIndia

Personalised recommendations