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Auto-correct-integrated trackers with and without memory of first frames

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Abstract

Visual short-term tracking in a long sequence of images with a lot of pose variations, target deformations and different types of occlusion is one of the harshest tasks in image processing. Overcoming such challenges can be performed in a superior way by combining different trackers. In this paper, we propose a method that combines different algorithms for tracking the given target. The algorithms are KCF (Henriques et al. in IEEE Trans Pattern Anal Mach Intell 37(3):583–596, 2014), MIL (Babenko et al. in Visual tracking with online multiple instance learning. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009), CSR-DCF (Lukezic et al. in Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017) and MOSSE (Bolme et al. in Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010) trackers. In this method, these algorithms can continuously correct each other’s faults. They also implicitly search for the target even when they are tracking it, to make sure the object they are tracking is the real target. Thus, they outperform many of the state-of-the-art trackers as well as their components when they work independently. In this paper, we examine three trackers which we made in such way. Two of these trackers run at speeds close to real-time on a CPU and so we compare them with some famous wide-spread tracking algorithms both in terms of accuracy and speed.

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References

  • Akshay, S., Sajin, T., Ram Prashanth, A.: Improved multiple object detection and tracking using KF-OF method. Int. J. Eng. Technol. 8 (2016)

  • Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2009)

  • Bertinetto, L., et al.: Fully-convolutional siamese networks for object tracking. In: European Conference on Computer Vision. Springer, Cham (2016)

  • Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE (2017)

  • Bolme, D.S., et al.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE (2010)

  • Bradski, G.R.: Computer vision face tracking as a component of a perceptual user interface. In: Workshop on Applications of Computer Vision, Princeton, NJ (1998–10) (1998a)

  • Bradski, G.R.: Real time face and object tracking as a component of a perceptual user interface. In: Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV’98 (Cat. No. 98EX201). IEEE (1998b)

  • Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)

    Article  Google Scholar 

  • Danelljan, M., et al.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, Nottingham, September 1–5, 2014. BMVA Press, (2014)

  • Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. Bmvc 1(5), 6 (2006)

    Google Scholar 

  • He, A., et al.: A twofold siamese network for real-time object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

  • Held, D., Thrun, S., Savarese, S.: Learning to track at 100 fps with deep regression networks. In: European Conference on Computer Vision. Springer, Cham (2016)

  • Henriques, J.F., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)

    Article  Google Scholar 

  • Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2011)

    Article  Google Scholar 

  • Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)

    Article  MathSciNet  Google Scholar 

  • Kristan, M., et al.: The visual object tracking vot2015 challenge results. In: Proceedings of the IEEE international conference on computer vision workshops (2015)

  • Kristan, M., et al.: The visual object tracking vot2016 challenge results. In: ECCV Workshop, vol. 2. no. 6. (2016)

  • Kristan, M., et al.: The visual object tracking vot2017 challenge results. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

  • Lee, H., Choi, S., Kim, C.: A memory model based on the siamese network for long-term tracking. In: Proceedings of the European Conference on Computer Vision (ECCV). (2018)

  • Li, B., et al.: High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

  • Lukezic, A., et al.: Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2017)

  • Patel, H.A., Thakore, D.G.: Moving object tracking using kalman filter. Int. J. Comput. Sci. Mob. Comput. 2(4), 326–332 (2013)

    Google Scholar 

  • Tran, A., Manzanera, A.: A versatile object tracking algorithm combining particle filter and generalised Hough transform. In: 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE (2015)

  • Wu, Yi, Lim, Jongwoo, Yang, Ming-Hsuan: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)

    Article  Google Scholar 

  • Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 13 (2006)

    Article  Google Scholar 

Download references

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Correspondence to Ali Sekhavati.

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Sekhavati, A., Eghbal, N. Auto-correct-integrated trackers with and without memory of first frames. Int J Intell Robot Appl 4, 191–201 (2020). https://doi.org/10.1007/s41315-020-00137-0

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  • DOI: https://doi.org/10.1007/s41315-020-00137-0

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