Skip to main content

Advertisement

SpringerLink
Log in

A Delay-Free and Robust Object Tracking Approach for Robotics Applications

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In many robotic applications there is the need for detecting and tracking moving and/or static objects while the robot moves, in order to interact with them. High quality detection methods require considerable computational time when the number of objects to be detected is high, or when operating within dynamic, real-world environments. Then, when an object detection result is available, it is referred to a previous frame and not to the current one. A method for obtaining delay-free detections is introduced in this present article. It consists of projecting a delayed detection onto the current frame by using a set of feature tracks generated by using the KLT (Kanade-Lucas-Tomasi) tracker. The proposed method is shown to improve detection accuracy when the tracked object is moving with respect to the camera. In addition, the method is able to detect and manage false detections and occlusions using statistical classifiers (Support Vector Machine) and the Viterbi algorithm (Viterbi, IEEE Trans. Inf. Theory 13(2), 260–269 1967). The method is validated in a person-following task, and compared against a part-based HOG person detector, and four performant tracking methods (Meanshift, Compressive Tracking, Tracking-by-detection with Kernels and Kernelized Correlation Filter). Additionally, the method is validated in two additional tasks: face tracking and car tracking. In all reported experiments, the proposed method obtains the best performance among all compared 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

Similar content being viewed by others

References

  1. Felzenszwalb, P., McAllester, D., Ramanan, D.: A Discriminatively Trained, Multiscale, Deformable Part Model. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1–8. CVPR 2008 (2008)

  2. Tomasi, C., Kanade, T.: Detection and Tracking of Point Features, School of Computer Science. Carnegie Mellon University, Pittsburgh (1991)

    Google Scholar 

  3. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  4. Zhang, K., Zhang, L., Yang, M.H.: Real-Time Compressive Tracking. In: Computer Vision–ECCV 2012, pp. 864–877 (2012)

  5. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the Circulant Structure of Tracking-By-Detection with Kernels. In: Computer Vision–ECCV 2012, pp. 702–715 (2012)

  6. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. vol. 2, pp. 142–149 (2000)

  7. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: Proceedings of the 2011 International Conference on Computer Vision, pp. 2564–2571 (2011)

  8. Alahi, A., Ortiz, O., Vandergheynst, P.: Freak: Fast Retina Keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517 (2012)

  9. Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn. 13(2), 111–122 (1981)

    Article  MATH  Google Scholar 

  10. Ruiz-Del-Solar, J., Loncomilla, P.: Robot head pose detection and gaze direction determination using local invariant features. Adv. Robot. 23(3), 305–328 (2009)

    Article  Google Scholar 

  11. Loncomilla, P., Ruiz-del-Solar, J., Martínez, L.: Object recognition using local invariant features for robotic applications: A survey. Pattern Recogn. 60, 499–514 (2016)

    Article  Google Scholar 

  12. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2008)

    Article  Google Scholar 

  13. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  14. Guo, Z., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  15. 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 (2005)

  16. Chu, C.T., Hwang, J.N., Pai, H.I., Lan, K.M.: Tracking human under occlusion based on adaptive multiple kernels with projected gradients. IEEE Trans. Multimed. 15(7), 1602–1615 (2013)

    Article  Google Scholar 

  17. Zhou, X., Li, Y., He, B.: Tracking Humans in Mutual Occlusion based on Game Theory (2013)

  18. Jeong, J.M., Yoon, T.S., Park, J.B.: Kalman filter based multiple objects detection-tracking algorithm robust to occlusion. In: 2014 Proceedings of the SICE Annual Conference (SICE), pp. 941–946 (2014)

  19. Rahmatian, S., Safabakhsh, R.: Online Multiple People Tracking-By-Detection in Crowded Scenes. In: 2014 7Th International Symposium on Telecommunications (IST), pp. 337–342 (2014)

  20. Suresh, S., Chitra, K., Deepack, P.: Patch Based Frame Work for Occlusion Detection in Multi Human Tracking. In: Circuits, Power and Computing Technologies (ICCPCT), pp. 1194–1196 (2013)

  21. Li, Z., Tang, Q.L., Sang, N.: Improved mean shift algorithm for occlusion pedestrian tracking. Electron. Lett. 44(10), 622–623 (2008)

    Article  Google Scholar 

  22. Yan, J., Ling, Q., Zhang, Y., Li, F., Zhao, F.: A Novel Occlusion-Adaptive Multi-Object Tracking Method for Road Surveillance Applications. In: 2013 32Nd Chinese Control Conference (CCC), pp. 3547–3551 (2013)

  23. Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., Schiele, B.: Learning People Detectors for Tracking in Crowded Scenes. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1049–1056 (2013)

  24. Tang, S., Andriluka, M., Schiele, B.: Detection and tracking of occluded people. Int. J. Comput. Vis. 110(1), 58–69 (2014)

    Article  Google Scholar 

  25. Guan, Y., Chen, X., Yang, D., Wu, Y.: Multi-Person Tracking-By-Detection with Local Particle Filtering and Global Occlusion Handling. In: 2014 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2014)

  26. Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 13(2), 260–269 (1967)

    Article  MATH  Google Scholar 

  27. Pairo, W., Ruiz-del-Solar, J., Verschae, R., Correa, M., Loncomilla, P.: Person Following by Mobile Robots: Analysis of Visual and Range Tracking Methods and Technologies. In: Robocup 2013: Robot World Cup XVII, pp. 231–243 (2014)

  28. Ruiz-del-Solar, J., Correa, M., Verschae, R., Bernuy, F., Loncomilla, P., Mascaró, M., Riquelme, R., Smith, F.: Bender – A general-purpose social robot with human-robot interaction abilities. J. Hum.–Robot Interact. 1(2), 54–75 (2012)

    Google Scholar 

  29. Uijlings, R., van de Sande, A., Gevers, T., Smeulders, M.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154 (2013)

    Article  Google Scholar 

  30. Zitnick, C.L., Dollár, P.: Edge Boxes: Locating object proposals from edges. In: ECCV 2014, Lecture Notes in Computer Science of Computer Vision, vol. 8639, pp. 391–405 (2014)

  31. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Visual object detection with deformable part models. Commun. ACM 56(9), 97–105 (2013)

    Article  Google Scholar 

  32. Dollár, P., Belongie, S.J., Perona, P.: The fastest pedestrian detector in the West. BMVC 2(3), 68.1–68.11 (2010)

    Google Scholar 

  33. Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Pernici, F., Del Bimbo, A.: Object tracking by oversampling local features. IEEE Trans. Pattern Anal. Mach. Intell. 36(12), 2538–2551 (2014)

    Article  Google Scholar 

  36. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet Classification with Deep Convolutional Neural Networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

  37. Girshick, R.: Fast R-CNN. arXiv:1504.08083 [cs.CV] (2015)

  38. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In: Advances in Neural Information Processing Systems (NIPS), Vol. 28 (2015)

  39. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

  40. Chen, L., Zhou, F., Shen, Y., Tian, X., Ling, H., Chen, Y.: Illumination insensitive efficient Second-Order minimization for planar object tracking. ICRA (2017)

  41. Tan, D.J., Ilic, S.: Multi-forest tracker: a chameleon in tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1202–1209 (2014)

  42. Tan, D.J., Tombari, F., Ilic, S., Navab, N.: A versatile learning-based 3d temporal tracker: Scalable, robust, online. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 693–701 (2015)

  43. Tan, D.J., Navab, N., Tombari, F.: Looking beyond the Simple Scenarios: Combining Learners and Optimizers in 3D Temporal Tracking. In: IEEE Transactions on Visualization and Computer Graphics (2017)

  44. Redmon, J., Farhadi, A.: YOLO9000: Better, Faster, Stronger. arXiv:1612.08242 (2016)

  45. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M.M., Hicks, S.L., Torr, P.H.S.: Struck: Structured output tracking with Kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(110), 2096–2109 (2016)

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially funded by FONDECYT Projects 1130153 and 1161500.

Author information

Authors and Affiliations

  1. Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile

    Wilma Pairo, Patricio Loncomilla & Javier Ruiz del Solar

  2. Department of Electrical Engineering, Universidad de Chile, Santiago, Chile

    Wilma Pairo, Patricio Loncomilla & Javier Ruiz del Solar

Authors
  1. Wilma Pairo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patricio Loncomilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javier Ruiz del Solar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patricio Loncomilla.

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

Pairo, W., Loncomilla, P. & del Solar, J.R. A Delay-Free and Robust Object Tracking Approach for Robotics Applications. J Intell Robot Syst 95, 99–117 (2019). https://doi.org/10.1007/s10846-018-0840-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-018-0840-6

Keywords

Search

Navigation