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A Survey on Object Detection and Tracking in a Video Sequence

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Proceedings of International Conference on Computational Intelligence

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Object detection and tracking are crucial and strenuous tasks in many computer vision applications. The algorithms for detecting and tracking objects in videos are manifold. This paper discusses different stages in object tracking. Also, it reviews various approaches available for detection, classification and tracking of objects in a video. Merits and demerits of the existing methods are depicted in this paper.

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References

  1. A. Yilmaz, O. Javed, M. Shah, Object tracking: a survey. ACM Comput. Surv. 38(4), 1–45 (2006)

    Article  Google Scholar 

  2. https://en.wikipedia.org/wiki/Optical_flow

  3. B. Shijila, A.J. Tom, S.N. George, Simultaneous denoising and moving object detection using low rank Approximation. Elsev. Fut. Generat. Comput. Syst. 90, 198–210 (2019)

    Article  Google Scholar 

  4. M. Babaee, D.T. Dinh, G. Rigolla, A deep convolutional neural network for video sequence background subtraction. Pattern Recogn. 76, 635–649 (2018)

    Google Scholar 

  5. Y. Wang, Z. Luo, P.-M. Jodoina, Interactive deep learning method for segmenting moving objects. Pattern Recogn. Lett. 96, 66–75 (2017)

    Google Scholar 

  6. R. Chen Richao, G. Yang Gaobo, N. Zhu Ningbo, Detection of object-based manipution by the statistical features of object contour. Forensic Sci. Int. 236, 164–169 (2014)

    Article  Google Scholar 

  7. Z. Zhong-Qiu, Z. Peng, X. Shou-tao W. Xindong, Object detection with deep learning: a review, J. Latex Class Files, 14 (2017)

    Google Scholar 

  8. J.R.R. Uijlings, K.E.A. Van De Sande, T. Gevers et al., Selective search for object recognition. Int. J. Comput. Vision 104, 154–171 (2013)

    Article  Google Scholar 

  9. R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  10. K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition 1–14 (2015). arXiv:1406.4729v4 [cs.CV]

  11. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. CVPR (2006)

    Google Scholar 

  12. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6) (2017)

    Google Scholar 

  13. L. Hei, D. Jia, CornerNet: detecting objects as paired keypoints. Eur. Conf. Comput. Vis. (ECCV), 1–17 (2018)

    Google Scholar 

  14. D. Kaiwen, B. Song, X. Lingxi, Q. Honggang, H. Qingming, T. Qi, Centernet: Keypoint triplets for object detection. IEEE Int. Conf. Comput Vis. (ICCV), 6568–6577 (2019)

    Google Scholar 

  15. T. Mingxing, P. Ruoming, V.L. Quoc, EfficientDet: Scalable and efficient object detection, in The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2020), pp. 10778–10787

    Google Scholar 

  16. R. Joseph, F. Ali, Yolo9000: Better, faster, stronger. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  17. A. Geiger, P. Lenz, C. Stiller, R. Urtasun, Vision meets robotics: the kitti dataset. Int. J. Rob. Res. 32(11), 1231–1237 (2013)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  19. A. kumar, M. Ananthasayanam, P.S. Rao, A constant gain Kalman filter approach for the prediction of re-entry of risk objects. Acta Astronaut 61(10), 831–839 (2007)

    Google Scholar 

  20. G. Cook, D.E. Dawson, Optimum constant-gain filters. IEEE Trans. Ind. Electron. Control Instrum. 21(3), 159–163 (1974)

    Article  Google Scholar 

  21. K. Wilson, An optimal control approach to designing constant gain filters. IEEE Trans. Aerosp. Electron. Syst. 8(6), 836–842 (1972)

    Article  Google Scholar 

  22. P.A. Yadav, N. Naik, M. Ananthasayanam (2013) A constant gain Kalman filter approach to track maneuvering targets, in Proceedings of the IEEE International Conference on Control Applications, 562–567.

    Google Scholar 

  23. N. Chernoguz, Adaptive-gain tracking filters based on minimization of the innovation variance, in International Conference on Acoustics, Speech, and Signal Processing, vol. 3 (2006)

    Google Scholar 

  24. H.Y. Chung, T.W. Kim, S.H. Chang, B. Lee, Adaptive Kalman gain approach to on-line instrument failure detection with improved GLR method and suboptimal control on loft pressurizer. IEEE Trans. Nucl. Sci. 33(4), 1103–1114 (1986)

    Article  Google Scholar 

  25. J.W. Almagbile, W. Ding, Evaluating the performances of adaptive Kalman filter methods in GPS/INS integration. J. Global Positioning Syst. 9(1), 33–40 (2010)

    Article  Google Scholar 

  26. H. Kaur, J.S. Sahambi, Vehicle tracking in video using fractional feedback Kalman filter. IEEE Trans. Comput. Imag. 2(4) (2016)

    Google Scholar 

  27. Z. H. E. Chen, Bayesian filtering : from Kalman filters to particle filters , and beyond sequential monte carlo estimation (2003)

    Google Scholar 

  28. N.G. Branko Ristic, S. Arulampalam, Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House, 2003)

    Google Scholar 

  29. D. Reid, An algorithm for tracking multiple targets. IEEE Trans. Autom. Control, 24(6), 843–854 (1979)

    Google Scholar 

  30. S. Blackman, R. Popoli, Design and Analysis of Modern Tracking Systems (Artech House Norwood, MA, 1999)

    Google Scholar 

  31. S.S. Blackman, Multiple hypothesis tracking for multiple target tracking. IEEE A&E Syst. Magaz. 19(1), 5–18 (2004)

    Article  Google Scholar 

  32. Y. Bar-Shalom, X.-R. Li, Multitarget-Multisensor Tracking: Principles and Techniques (YBS Publishing, Storrs, CT, 1995)

    Google Scholar 

  33. Y. Bar-Shalom, E. Tse, Tracking in a cluttered environment with probabilistic data association. Automatica 11, 451–460 (1975)

    Article  Google Scholar 

  34. S.-W. Joo, R. Chellappa, A Multiple-hypothesis approach for multiobject Visual tracking. IEEE Transactions on Image Processing, 16(11) (2007)

    Google Scholar 

  35. S.P. Coraluppi, C.A. Carthel, Multiple-hypothesis tracking for targets producing multiple measurements. IEEE Trans. Aerosp. Electron. Syst. 54(3) (2018)

    Google Scholar 

  36. C.-Y. Chong, S. Mori, D.B. Reid, 21st International Conference on Information Fusion (FUSION) (2018)

    Google Scholar 

  37. D. Comaniciu, V. Ramesh, P. Meer, “Real-time tracking of non-rigid objects using mean shift, in Proceeding IEEE Conference on Computer Vision and Pattern Recognition, vol. 2 ( (Hilton Head, SC, 2000), pp. 142–149

    Google Scholar 

  38. D. Comaniciu, V. Ramesh, P. Meer, Kernel-based object tracking. IEEE Trans. Pattern Analys. Mach. Intell. 25(5), 564–577 (2003)

    Article  Google Scholar 

  39. J. Ning, L. Zhang, D. Zhang, C. Wu, Robust mean shift tracking with corrected background-weighted histogram. IET Comput. Vision 6(1), 62–69 (2012)

    Article  MathSciNet  Google Scholar 

  40. X. An, J. Kim, Y. Han, Optimal colour-based mean shift algorithm for tracking objects. IET Comput. Vision 8(3), 235–244 (2014)

    Article  Google Scholar 

  41. D.T. Stathaki, Mean shift tracking through scale and occlusion. IET Sig. Proc. 6(5), 534–540 (2012)

    Article  MathSciNet  Google Scholar 

  42. C. Beyan, A. Temizel, Adaptive mean-shift for automated multi object tracking. IET Comput. Vision 6(1), 1–12 (2012)

    Article  MathSciNet  Google Scholar 

  43. Yu. Wangsheng, X. Tian, Z. Hou, Y. Zha, Y. Yang, Multi-scale mean shift tracking. IET Comput. Vision 9(1), 110–123 (2015)

    Article  Google Scholar 

  44. S. Zhang, Yu. Xin, Y. Sui, S. Zhao, Li. Zhang, Object tracking with multi-view support vector machines. IEEE Trans. Multimedia 17(3), 265–278 (2015)

    Google Scholar 

  45. Z. Xie, Z. Wei, C. Bai, Multi-aircrafts tracking using spatial–temporal constraints-based intra-frame scale-invariant feature transform feature matching. IET Comput. Vis. 9(6), 831–840 (2015)

    Article  Google Scholar 

  46. S. Zhang, Y. Sui, S. Zhao, Li. Zhang, Graph-regularized structured support vector machine for object tracking. IEEE Trans. Circ. Syst. Video Technol. 27(6), 1249–1262 (2017)

    Article  Google Scholar 

  47. Y. Yin, Xu. De, X. Wang, M. Bai, Online state-based structured SVM combined with incremental PCA for robust visual tracking. IEEE Trans. Cybernet. 45(9), 1988–2000 (2015)

    Article  Google Scholar 

  48. S. Hare, S. Golodetz, A. Saffari, V. Vineet, M.-M. Cheng, S.L. Hicks, H. Philip, Struck: “structured output tracking with kernels.” IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016)

    Article  Google Scholar 

  49. X. Cao, X. Jiang, X. Li, P. Yan, Correlation-based tracking of multiple targets with hierarchical layered structure. IEEE Trans. Cybernet. 48(1), 90–102 (2018)

    Article  Google Scholar 

  50. V. Ablavsky, S. Sclaroff, Layered graphical models for tracking partially occluded object. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1758–1775 (2011)

    Article  Google Scholar 

  51. S. Gao, Z. Han, Ce. Li, Q. Ye, J. Jiao, Real-time multi pedestrian tracking in traffic scenes via an RGB-D-based layered graph model. IEEE Trans. Intell. Transp. Syst. 16(5), 2814–2825 (2015)

    Article  Google Scholar 

  52. N. Wang, S. Li, A. Gupta, D.-Y. Yeung, Transferring rich feature hierarchies for robust visual tracking. [Online] (2015). Available: https://arxiv.org/abs/1501.04587

  53. S. Hong, T. You, S. Kwak, B. Han, Online tracking by learning discriminative saliency map with convolutional neural network. [Online] (2015). Available: https://arxiv.org/abs/1502.06796

  54. H. Nam, B. Han, Learning multi-domain convolutional neural networks for visual tracking. [Online] (2015). Available: https://arxiv.org/abs/1510.07945

  55. S. Yun, et al, Action-driven visual object tracking with deep reinforcement learning. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2239–2252 (2018)

    Google Scholar 

  56. Y. Wu, J. Lim, M.H. Yang, Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)

    Article  Google Scholar 

  57. M. Kristan, et al., The visual object tracking VOT2014 challenge results, in Processing European Conference on Computer Vision, Workshops, (2014), pp. 191–217

    Google Scholar 

  58. H. Zhaohui, L. Guixi, Z. Haoyang, Correlation filter-based visual tracking via adaptive weighted CNN features fusion. IET Image Proc. 12, 1423–1431 (2018)

    Article  Google Scholar 

  59. J. Chung, K. Sohn, Image-based learning to measure traffic density using a deep convolutional neural network. IEEE Trans. Intell. Transp. Syst. 19(5), 1670–1675 (2018)

    Article  Google Scholar 

  60. Z. He, Z. Zhang, C. Jung, Fast fourier transform networks for object tracking based on correlation filter. IEEE Access 6, 6594–6601 (2018)

    Article  Google Scholar 

  61. K. Chen, W. Tao, Convolutional regression for visual tracking. IEEE Trans. Image Process. 27(7), 3611–3620 (2018)

    Article  MathSciNet  Google Scholar 

  62. H. David, T. Sebastian, S. Silvio, Learning to Track at 100 FPS with Deep Regression Networks (2016) CoRR, abs/1604.01802

    Google Scholar 

  63. Z. Bewley, L. Ge, F.R. Ott, Upcroft, B., Simple online and realtime tracking, in IEEE International Conference on Image Processing (ICIP), (2016), pp. 3464–3468

    Google Scholar 

  64. N. Wojke, A. Bewley, D. Paulus, Simple online and realtime tracking with a deep association metric, in IEEE International Conference on Image Processing (ICIP) (2017), pp. 3645–3649

    Google Scholar 

  65. C. Qi, O. Wanli, L. Hongsheng, W. Xiaogang, L. Bin, Y.Nenghai, Online multi-object tracking using cnn-based single object tracker with spatial-temporal attention mechanism, in The IEEE International Conference on Computer Vision (ICCV) (2017), pp. 4836–4845

    Google Scholar 

  66. W. Lijun, O. Wanli, W. Xiaogang, L. Huchuan, Visual tracking with fully convolutional networks, in The IEEE International Conference on Computer Vision (ICCV), (2015), pp. 3119–3127

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India

    T. Sugirtha & M. Sridevi

Authors
  1. T. Sugirtha
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  2. M. Sridevi
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Editors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology, Pune, Pune, Maharashtra, India

    Ritu Tiwari

  2. Department of Computer Science and Engineering, Indian Institute of Information Technology, Pune, Pune, Maharashtra, India

    Apoorva Mishra

  3. Department of Mathematics & Scientific Computing, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, India

    Neha Yadav

  4. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Mario Pavone

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Sugirtha, T., Sridevi, M. (2022). A Survey on Object Detection and Tracking in a Video Sequence. In: Tiwari, R., Mishra, A., Yadav, N., Pavone, M. (eds) Proceedings of International Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-3802-2_2

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