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Adaptive Object Tracking Using Algorithms Employing Machine Learning

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Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019) (ICCBI 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 49))

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Abstract

Object tracking is considered as one of the most crucial areas of research. Real time applications of object tracking include video surveillance, vehicle tracking and so on. In this paper, solutions for adaptive tracking of moving objects using algorithms that employ machine learning are discussed. This paper discusses the following machine learning tracking algorithms; Multiple Instance Learning Track (MIL Track), Tracking-Learning-Detecting (TLD), Multi Domain Network (MDNet) and Circulant Structure Kernel tracker (CSK). MIL Track algorithm uses a special classifier to generate an adaptive appearance model. TLD is a tracking framework that is based on the processes of tracking learning and detection. MDNet uses Convolutional Neural Network for training the tracker. The depth analysis method employs CSK to handle occlusions. These tracking algorithms use techniques that have proven to be successful in overcoming the adaptive object tracking problems. These problems and their corresponding solutions are discussed further in this paper.

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References

  1. Babenko, B., Yang, M., Belongie, S.: Visual tracking with online Multiple Instance Learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 983–990 (2009)

    Google Scholar 

  2. Viola, P., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: Conference on Neural Information Processing Systems, pp. 1417–1426 (2005)

    Google Scholar 

  3. Oza, N.C.: Online ensemble learning. Ph. D. thesis, University of California, Berkeley (2001)

    Google Scholar 

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

    Article  Google Scholar 

  5. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  6. Wang, N., Li, S., Gupta, A., Yeung, D.-Y.: Transferring rich feature hierarchies for robust visual tracking. ArXiv preprint arXiv:1501.04587 (2015)

  7. Hong, S., You, T., Kwak, S., Han, B.: Online tracking by learning discriminative saliency map with convolutional neural network. In: International Conference on Machine Learning (2015)

    Google Scholar 

  8. Doll´ar, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  9. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Conference on Computer Vision and Pattern Recognition (2001)

    Google Scholar 

  10. Sung, K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–51 (1998)

    Article  Google Scholar 

  11. Wang, J., Chen, X., Gao, W.: Online selecting discriminative tracking features using particle filter. In: Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1037–1042 (2005)

    Google Scholar 

  12. Lee, B., Liew, L., Cheah, W., Wang, Y.: Occlusion handling in videos object tracking: a survey. In: IOP Conference Series (2018)

    Google Scholar 

  13. Bashar, A.: Survey on evolving deep learning neural network architectures. J. Artif. Intell. 1(02), 73–82 (2019)

    Google Scholar 

  14. Liu, C., Huynh, D.Q., Reynolds, M.: Toward occlusion handling in visual tracking via probabilistic finite state machines. In: IEEE Trans. Cybern. 1–13 (2018)

    Google Scholar 

  15. Henriques, F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of 12th European Conference on Computer Vision (2012)

    Chapter  Google Scholar 

  16. Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)

    Article  Google Scholar 

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Authors and Affiliations

  1. I.T. Department, Mukesh Patel School of Technology Management and Engineering, Mumbai, India

    Shubham Rai & Rejo Mathew

Authors
  1. Shubham Rai
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  2. Rejo Mathew
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Corresponding author

Correspondence to Shubham Rai .

Editor information

Editors and Affiliations

  1. Department of CSE, Vaigai College of Engineering, Melur, Tamil Nadu, India

    A. Pasumpon Pandian

  2. Department of Business Administration, The Gerald Schwartz School of Business, StFX University, Antigonish, NS, Canada

    Ram Palanisamy

  3. Electrical and Computer Engineering, University of Applied Sciences, Egaleo, Attiki, Greece

    Klimis Ntalianis

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Rai, S., Mathew, R. (2020). Adaptive Object Tracking Using Algorithms Employing Machine Learning. In: Pandian, A., Palanisamy, R., Ntalianis, K. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). ICCBI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-43192-1_44

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