Skip to main content
SpringerLink
Log in

A polar model for fast object tracking in 360-degree camera images

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

Abstract

The task of fast object tracking in polar images using emerging high-resolution 360-degree camera technology is presented in this paper. In this approach, when an arbitrary object has been selected in the first frame, the proposed method searches for the object in the next frames. This task is challenging when the video contains complexity which cannot be handled by common tracking methods. The main contribution of this paper uses polar object selection and color binary features to facilitate robust object tracking in 360-degree images. Using the proposed polar object selection method, each object is represented by a polar component and high performance of the tracking algorithm in terms of precision and speed is achieved. We evaluate the applicability of our approach on a new dataset containing more than 30000 frames of 360-degree images wherein high performance in challenging real-world scenarios is demonstrated. The proposed algorithm outperforms the related methods.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Bay H, Ess A, Tuytelaars T, Gool L (2008) Speeded up robust features (surf). J Comput Vis Image Underst (CVIU) 110(3):346–359

    Article  Google Scholar 

  2. Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr P (2016) Staple: complementary learners for real-time tracking. In: IEEE international conference on computer vision and pattern recognition (CVPR’16), pp 1401–1409

  3. Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: IEEE international conference on computer vision and pattern recognition (CVPR’10), pp 2544–2550

  4. Bouguet JY (2000) Pyramidal implementation of the Lucas Kanade feature tracker. Intel Corporation, Microprocessor Research Labs

  5. Calonder M, Lepetit V, Strecha C, Fua P (2010) Brief: binary robust independent elementary features. Proc. ECCV, pp 778–792

  6. Chen CH, Yao Y, Page D, Abidi B, Koschan A, Abidi M (2008) Heterogeneous fusion of omnidirectional and ptz cameras for multiple object tracking. IEEE Trans Circuits Syst Video Technol 18(8):1052–1063

    Article  Google Scholar 

  7. Choe G, Wang T, Liu F, Li G, Hyongwang O, Kim S (2015) Moving object tracking based on geogram. Multimed Tools Appl 74(21):9971–9794

    Article  Google Scholar 

  8. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low- and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002

    Article  Google Scholar 

  9. Delforouzi A, Tabatabaei SAH, Shirahama K, Grzegorzek M (2016) Polar object tracking in 360-degree camera images. In: 2016 IEEE international symposium on multimedia (ISM). https://doi.org/10.1109/ISM.2016.0077, pp 347–352

  10. Delforouzi A, Tabatabaei SAH, Shirahama K, Grzegorzek M (2016) Unknown object tracking in 360-degree camera images. In: 23rd international conference on pattern recognition, pp 1799–1804

  11. Demiroz BE, Ar I, Eroglu O, Salah AA, Akarun L (2012) Feture-based tracking on a multi-omnidirectional camera dataset. In: International symposium on communications, control and signal processing, pp 1–5

  12. Ding G, Chen W, Zhao S, Han J, Liu Q (2018) Real-time scalable visual tracking via quadrangle kernelized correlation filters. IEEE Trans Intell Transp Syst 19 (1):140–150

    Article  Google Scholar 

  13. Foldesy P, Szatmari I, Zarandy A (2002) Moving object tracking on panoramic images. In: Proceedings of the 2002 7th IEEE international workshop on, pp 63–70

  14. Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Article  Google Scholar 

  15. Hrabar S, Sukhatme GS (2003) Omnidirectional vision for an autonomous helicopter. In: Proceedings. ICRA ’03 IEEE international conference on robotics and automation, 2003, vol 1, pp 558–563

  16. Kalal Z, Matas J, Mikolajczyk K (2010) P-n learning: boot-strapping binary classifiers by structural constrains. In: IEEE conference on computer vision and pattern recognition, pp 49–56

  17. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422

    Article  Google Scholar 

  18. Kim J, Suga Y (2007) An omnidirectional vision-based moving obstacle detection in mobile robot. Int J Control Autom Syst 5(6):663–673

    Google Scholar 

  19. Leibe B, Seemann E, Schiele B (2005) Pedestrian detection in crowded scenes. In: Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition, pp 878–885

  20. Lin Z, Davis L S, Doermann D, DeMenthon D (2007) Hierarchical part-template matching for human detection and segmentation. In: IEEE 11th international conference on computer vision, 2007. ICCV 2007, pp 1–8

  21. Liu KC, Shen YT, Chen LG (2018) Simple online and realtime tracking with spherical panoramic camera. In: IEEE international conference on consumer electronics (ICCE), p 2018

  22. Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: 21st international conference on pattern recognition (ICPR 2012)

  23. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: recognizing complex activities from sensor data. In: Proceedings of the international joint conference on artificial intelligence, pp 1617–1623

  24. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  25. Markovic I, Chaumette F, Petrovic I (2014) Moving object detection, tracking and following using an omnidirectional camera on a mobile robot. In: IEEE international conference on robotics and automation (ICRA’14), pp 5630–5635

  26. Miao Q, Wang G, Shi C, Lin X, Ruan Z (2011) A new framework for on-line object tracking based on surf. Pattern Recogn Lett 32(13):1564–1571

    Article  Google Scholar 

  27. Milan A, Roth S, Schindler K (2014) Continuous energy minimization for multitarget tracking. IEEE Trans Pattern Anal Mach Intell 36(1):58–72

    Article  Google Scholar 

  28. Milan A, Schindler K, Roth S (2015) Multi-target tracking by discrete-continuous energy minimization. IEEE Trans Pattern Anal Mach Intell 38(10):2054–2068

    Article  Google Scholar 

  29. Oh C, Lee Y, Kim D, Lee C (2012) Moving object detection in omnidirectional vision-based mobile robot. IEEE IECON, pp 4232–4235

  30. Scaramuzza D, Siegwart R (2008) Appearance-guided monocular omnidirectional visual odometry for outdoor ground vehicles. IEEE Trans Robot 24(5):1015–1026

    Article  Google Scholar 

  31. Scotti G, Marcenaro L, Coelho C, Selvaggi F, Regazzoni CS (2004) A novel dual camera intelligent sensor for high definition 360 degrees surveillance. In: IEEE intelligent distributed surveilliance systems, pp 26–30

  32. Shuoa H, Nab W, Huajunc S (2012) Object tracking method based on surf. Appl Mech Mater 3:351–356. https://www.sciencedirect.com/science/article/pii/S2212671612002144

    Google Scholar 

  33. Walia GS, Kapoor R (2016) High-speed tracking with kernelized correlation filters. Multimed Tools Appl 75(23):583–596

    Article  Google Scholar 

  34. Wang D, Zhang Q, Morris J (2012) Distributed markov chain monte carlo kernel based particle filtering for object tracking. Multimed Tools Appl 56(2):303–314

    Article  Google Scholar 

  35. Wang L, Yung NHC (2012) Three-dimensional model-based human detection in crowded scenes. IEEE Trans Intell Transp Syst 13(2):691–703

    Article  Google Scholar 

  36. Wu B, Nevatia R (2007) Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors. Int J Comput Vis 75 (2):247–266

    Article  Google Scholar 

  37. Xiao J, Stolkin R, Leonardis A (2015) Single target tracking using adaptive clustered decision trees and dynamic multi-level appearance models. In: IEEE conference on computer vision and pattern recognition, pp 4978–4987

  38. Zhang B, Li Z, Cao X, Ye Q, Chen C, Shen L, Perina A, Ji R (2017) Output constraint transfer for kernelized correlation filter in tracking. IEEE Trans Syst Man Cybern Syst 47(4):693–703

    Article  Google Scholar 

  39. Zhao S, Yao H, Gao Y, Ding G, Chua T (2016) Predicting personalized image emotion perceptions in social networks. IEEE Trans Affect Comput X:1–14. https://ieeexplore.ieee.org/document/7744512/

    Google Scholar 

  40. Zhao S, Gao Y, Ding G, Chua TS (2017) Real-time multimedia social event detection in microblog. IEEE Trans Cybern PP(99):1–14

    Google Scholar 

  41. Zhao S, Yao H, Gao Y, Ji R, Ding G (2017) Continuous probability distribution prediction of image emotions via multitask shared sparse regression. IEEE Trans Multimed 19(3):632–645

    Article  Google Scholar 

  42. Zirakchi A, Lundberg CL, Sevil HE (2017) Omni directional moving object detection and tracking with virtual reality feedback. In: ASME 2017 dynamic systems and control conference

  43. Zivkovic Z, Heijden FVD (2006) Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn Lett 27(7):773–780

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Group for Pattern Recognition, University of Siegen, Hoelderlinstr. 3, D-57076, Siegen, Germany

    Ahmad Delforouzi, Seyed Amir Hossein Tabatabaei, Kimiaki Shirahama & Marcin Grzegorzek

Authors
  1. Ahmad Delforouzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Amir Hossein Tabatabaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kimiaki Shirahama
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcin Grzegorzek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Delforouzi.

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

Delforouzi, A., Tabatabaei, S.A.H., Shirahama, K. et al. A polar model for fast object tracking in 360-degree camera images. Multimed Tools Appl 78, 9275–9297 (2019). https://doi.org/10.1007/s11042-018-6525-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6525-0

Keywords

Search

Navigation