Skip to main content
SpringerLink
Log in

Object tracking method based on data computing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The data computing process is utilized in various areas such as autonomous driving. Autonomous vehicles are intended to detect and track nearby moving objects avoiding collisions and to navigate in complex situations, such as heavy traffic and dense pedestrian areas. Therefore, object tracking is the core technology in the environment perception systems of autonomous vehicles and requires the monitoring of surrounding objects and the prediction of the moving states of objects in real time. In this paper, a multiple object tracking method based on light detection and ranging (LiDAR) data is proposed by using a Kalman filter and data computing process. We suppose that the movements of the tracking objects are captured consecutively as frames; thus, model-based detection and tracking of dynamic objects are possible. A Kalman filter is applied for predicting posterior state of tracking object based on anterior state of the tracking object. State denotes the positions, shapes, and sizes of objects. By computing the likelihood probability between predicted tracking objects and clusters which registered from tracking objects, the data association process of the tracking objects can be generated. Experimental results showed enhanced object tracking performance in a dynamic environment. The average matching probability of the tracking object was greater than 92.9%.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Kang J, Cohen I, Medioni G, Yuan C (2005) Detection and tracking of moving objects from a moving platform in presence of strong parallax. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, pp 10–17

  2. Rahman AH, Zamzuri H, Mazlan SA, Rahman MA (2014) Model-based detection and tracking of single moving object using laser range finder. In: Proceedings of 2014 5th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp 556–561

  3. Brookhuis KA, De Waard D, Janssen WH (2013) Behavioural impacts of advanced driver assistance systems—an overview. Eur J Transp Infrastruct Res 1(3):245–253

    Google Scholar 

  4. Chu PM, Cho S, Fong S, Park YW, Cho K (2017) 3D reconstruction framework for multiple remote robots on cloud system. Symmetry 9(4):55

    Article  MathSciNet  Google Scholar 

  5. Dewan A, Caselitz T, Tipaldi GD, Burgard W (2016) Motion-based detection and tracking in 3D LiDAR scans. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp 4508–4513

  6. Pauly M, Keiser R, Kobbelt LP, Gross M (2003) Shape modeling with point-sampled geometry. ACM Trans Graph (TOG) 22(3):641–650

    Article  Google Scholar 

  7. Howard TM, Pivtoraiko M, Knepper RA, Kelly A (2014) Model-predictive motion planning: several key developments for autonomous mobile robots. IEEE Robot Autom Mag 21(1):64–73

    Article  Google Scholar 

  8. Shantaiya S, Verma K, Mehta K (2015) Multiple object tracking using Kalman filter and optical flow. Eur J Adv Eng Technol 2(2):34–39

    Google Scholar 

  9. Rao M, Kamila NK (2017) Tracking intruder ship in wireless environment. Hum Centric Comput Inf Sci 7(1):14

    Article  Google Scholar 

  10. Hu W, Tan T, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern Part C (Appl Rev) 34(3):334–352

    Article  Google Scholar 

  11. Cho H, Seo YW, Kumar BV, Rajkumar RR (2014) A multi-sensor fusion system for moving object detection and tracking in urban driving environments. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp 1836–1843

  12. Premebida C, Monteiro G, Nunes U, Peixoto P (2007) A LiDAR and vision-based approach for pedestrian and vehicle detection and tracking. In: Proceedings of Intelligent Transportation Systems Conference, pp 1044–1049

  13. Pauly M, Keiser R, Gross M (2003) Multi-scale feature extraction on point-sampled surfaces. Comput Graph Forum 22(3):287–289

    Article  Google Scholar 

  14. Wang CC, Thorpe C, Thrun S, Hebert M, Durrant-Whyte H (2007) Simultaneous localization, mapping and moving object tracking. Int J Robot Res 26(9):889–916

    Article  Google Scholar 

  15. Dissanayake MG, Newman P, Clark S, Durrant-Whyte HF, Csorba M (2001) A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans Robot Autom 17(3):229–241

    Article  Google Scholar 

  16. Wittmann D, Chucholowski F, Lienkamp M (2014) In: Proceedings of 2014 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), vol 1, pp 794–801

  17. Miyasaka T, Ohama Y, Ninomiya Y (2009) Ego-motion estimation and moving object tracking using multi-layer LiDAR. In: Intelligent Vehicles Symposium, pp 151–156

  18. Steinhauser D, Ruepp O, Burschka D (2008) Motion segmentation and scene classification from 3D LiDAR data. In: Proceedings of Intelligent Vehicles Symposium, pp 398–403

  19. Wang DZ, Posner I, Newman P (2015) Model-free detection and tracking of dynamic objects with 2D LiDAR. Int J Robot Res 34(7):1039–1063

    Article  Google Scholar 

  20. Choi J, Ulbrich S, Lichte B, Maurer M (2013) Multi-target tracking using a 3D-LiDAR sensor for autonomous vehicles. In: Proceedings of 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC), pp 881–886

  21. Grinberg M, Ohr F, Beyerer J (2009) Feature-based probabilistic data association (FBPDA) for visual multi-target detection and tracking under occlusions and split and merge effects. In: Proceedings of 12th International IEEE Conference on Intelligent Transportation Systems, pp 1–8

  22. Ye Y, Fu L, Li B (2016) Object detection and tracking using multi-layer laser for autonomous urban driving. In: Proceedings of IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp 259–294

  23. Chang TH, Gong S (2001) Tracking multiple people with a multi-camera system. In: Proceedings of 2001 IEEE Workshop on Multi-object Tracking, pp 19–26

  24. Chu P, Cho S, Sim S, Kwak K, Cho K (2017) A fast ground segmentation method for 3D point cloud. J Inf Process Syst 13(3):491–499

    Google Scholar 

  25. Song W, Liu L, Tian Y, Sun G, Fong S, Cho K (2017) A 3D localisation method in indoor environments for virtual reality applications. Hum Centric Comput Inf Sci 7(1):39

    Article  Google Scholar 

  26. Li X, Wang K, Wang W, Li Y (2010) A multiple object tracking method using Kalman filter. In: Proceedings of 2010 IEEE International Conference on Information and Automation (ICIA), pp 1862–1866

  27. Vermaak J, Godsill SJ, Perez P (2005) Monte Carlo filtering for multi target tracking and data association. In: Proceedings of 2005 IEEE Transactions on Aerospace and Electronic Systems, vol 41, no 1, pp 309–332

  28. Carlevaris-Bianco N, Ushani AK, Eustice RM (2016) University of Michigan North Campus long-term vision and LiDAR dataset. Int J Robot Res 35(9):1023–1035

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2015R1A2A2A01003779) and by BK21 Plus project of the National Research Foundation of Korea Grant.

Author information

Authors and Affiliations

  1. Department of Multimedia Engineering, Dongguk University-Seoul, 30, Pildongro-1-gil, Jung-gu, Seoul, 04620, Republic of Korea

    Weiqiang Zhang, Seoungjae Cho, Jeongsook Chae, Yunsick Sung & Kyungeun Cho

Authors
  1. Weiqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seoungjae Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeongsook Chae
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunsick Sung
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kyungeun Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kyungeun Cho.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this manuscript.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, W., Cho, S., Chae, J. et al. Object tracking method based on data computing. J Supercomput 75, 3217–3228 (2019). https://doi.org/10.1007/s11227-018-2535-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2535-y

Keywords

Search

Navigation