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Efficient fuzzy feature matching and optimal feature points for multiple objects tracking in fixed and active camera models

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Abstract

In computer vision, the multiple objects tracking play a vital challenging role. To solve the issues in this research field, various traditional techniques had been developed. In this paper, we consider the problem of tracking multiple persons in a dynamic environment (background) such as illumination changes and shadow moving. Notably, i) Estimating camera motion and ii) Multiple persons tracking are the two main phases involved in our proposed approach. In the first phase, the good features were extracted using both the SIFT features extraction steps and Gaussian noise elimination method. Instead of using the conventional SIFT-based matching method, we have introduced a new fuzzy matching method to create an adaptive matching zone (region). Using this, the two corresponding features from different frames can be matched perfectly. The brightness of a matching feature of interest indicates its size. Additionally, we use the magnitude and direction of the motion for accurate elimination of camera motion. In the second phase, the persons are tracked from the moving object by finding the optimal feature points and clustering of final points are made as the moving persons (objects). Experimental validation was performed on different challenging datasets and promising results are achieved by our proposed method compared to other existing methods.

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References

  1. Andriyenko A, Schindler K, Roth S (2012) Discrete-continuous optimization for multi-target tracking. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1926–1933

  2. Bae S-H, Yoon K-J (2014) “Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning.” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1218–1225

  3. Bae S-H, Yoon K-J (2018) Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking. IEEE Trans Pattern Anal Mach Intell 40(3):595–610

    Article  Google Scholar 

  4. Berclaz J, Fleuret F, Turetken E, Fua P (2011) Multiple object tracking using k-shortest paths optimization. IEEE Trans Pattern Anal Mach Intell 33:1806–1819

    Article  Google Scholar 

  5. Bernardin K, Stiefelhagen R (2008) Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J Image Video Process 2008(1):1–10

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. Breitenstein MD, Reichlin F, Leibe B, Koller-Meier E, Van Gool L (2011) Online multiperson tracking by-detection from a single, uncalibrated camera. IEEE Trans Pattern Anal Mach Intell 33:1820–1833

    Article  Google Scholar 

  8. Brendel W, Amer M, Todorovic S (2011) Multiobject tracking as maximum weight independent set. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1273–1280

  9. Chen Q, Zhang G, Yang X, Li S, Li Y, Wang HH (2018) Single image shadow detection and removal based on feature fusion and multiple dictionary learning. Multimed Tools Appl 77(14):18601–18624

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Doyle DD, Jennings AL, Black JT (2014) Optical flow background estimation for real-time pan/tilt camera object tracking. Measurement 48:195–207

    Article  Google Scholar 

  12. Fagot-Bouquet L, Audigier R, Dhome Y, Lerasle F (2016) Improving Multi-Frame Data Association with Sparse Representations for Robust Near-Online Multi-Object Tracking, European Conference on Computer Vision (ECCV). pp.774–790

  13. Vinu Sundararaj, (2019). Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Communications, 104(1), 173–197.

  14. Feng P, Xu C, Zhao Z, Liu F, Guo J, Yuan C, Wang T, Duan K (2018) A deep features based generative model for visual tracking. Neurocomputing. 308(25):245–254

    Article  Google Scholar 

  15. He S, Yang Q, Lau RW, Wang J, Hyang M (2013) Visual tracking via locality sensitive histograms. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 2427–2434

  16. Hong Yoon J, Lee C-R, Yang M-H, Yoon K-J (2016) “Online multi-object tracking via structural constraint event aggregation,” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1392–1400

  17. Horbert E, Rematas K, Leibe B (2011) Level-set person segmentation and tracking with multi-region appearance models and top-down shape information. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp 1871–1878

  18. Hsieh YS, Su YC, Chen LG (2012) Robust moving object tracking and trajectory prediction for visual navigation in dynamic environments. In: IEEE International Conference on Consumer Electronics, pp 696–697

  19. Hu Q, Guo Y, Lin Z, Deng X, An W (2017) Fast correlation tracking using low-dimensional scale filter and local search strategy. IEEE Access 5(99):8568–8578

    Article  Google Scholar 

  20. Hu H, Ma B, Shen J, Shao L (2018) Manifold regularized correlation object tracking. IEEE transactions on neural networks and learning systems 29(5):1786–1795

    Article  MathSciNet  Google Scholar 

  21. Huang C, Li Y, Nevatia R (2013) Multiple target tracking by learning-based hierarchical association of detection responses. IEEE Trans Pattern Anal Mach Intell 35:898–910

    Article  Google Scholar 

  22. Jiang H, Fels S, Little JJ (2007) A linear programming approach for multiple object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07, pp 1–8

  23. Jung B, Sukhatme GS (2004) Detecting moving objects using a single camera on a mobile robot in an outdoor environment. In: International Conference on Intelligent Autonomous Systems, pp 980–987

  24. Kim S, Lee S, Kim S, Lee J (2009) Object tracking of mobile robot using moving color and shape information for the a ged walking. Int J Adv Sci Technol 3:59–68

    Google Scholar 

  25. Kuo C-H, Huang C, Nevatia R (2010) “Multi-target tracking by on-line learned discriminative appearance models.” In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 685–692

  26. Leal-Taixé L, Canton-Ferrer C, Schindler K (2016) Learning by Tracking: Siamese CNN for Robust Target Association. Deep Vision Workshop (CVPR). Las Vegas (Nevada, USA), 2016, pp.33–40

  27. Leibe B, Schindler K, Cornelis N, Van Gool L (2008) Coupled object detection and tracking from static cameras and moving vehicles. IEEE Trans Pattern Anal Mach Intell 30:1683–1698

    Article  Google Scholar 

  28. Li K, He FZ, Yu HP (2018) Robust visual tracking based on convolutional features with illumination and occlusion handing. J Comput Sci Technol 33(1):223–236

    Article  Google Scholar 

  29. Liang-qun L, Xi-yang Z, Zong-xiang L, Wei-xin X (2018) Fuzzy logic approach to visual multi-object tracking. Neurocomputing 281(15):139–151

    Article  Google Scholar 

  30. Lim JS, Kim WH (2005) Detection and tracking multiple pedestrians from a moving camera. Lect Notes Comput Sci 3804:527–534

    Article  Google Scholar 

  31. Lim JS, Kim WH (2013) Detecting and tracking of multiple pedestrians using motion, color information and the AdaBoost algorithm. Multimed Tools Appl 65(1):161–179

    Article  Google Scholar 

  32. Lim JS, Kim WH (2013) Detecting and tracking of multiple pedestrians using motion, color information and the AdaBoost a lgorithm. Multimed Tools Appl 65(1):161–179

    Article  Google Scholar 

  33. Lin Y, Wang B, Zhao Y (2018) Moving shadow detection using fusion of multiple features. In: Advances in computer and computational sciences. Springer, Singapore, pp 487–494

    Chapter  Google Scholar 

  34. Liu J, Cao X, Li Y, Zhang B (2018) Online multi-object tracking using hierarchical constraints for complex scenarios. IEEE Trans Intell Transp Syst 19(1):151–161

    Article  Google Scholar 

  35. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Milan A, Leal-Taixé L, Schindler K, Reid I (2015) Joint tracking and segmentation of multiple targets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5397–5406

  38. Milan A, Rezatofighi S, Dick A, Reid I, Schindler K (2017) Online Multi-Target Tracking using Recurrent Neural Networks, In AAAI. pp.1–9

  39. Pirsiavash H, Ramanan D, Fowlkes CC (2011) Globally-optimal greedy algorithms for tracking a variable number of objects. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1201–1208

  40. Siam M, ElSayed R, ElHelw M (2012) On-board multiple target detection and tracking on camera equipped aerial vehicles. IEEE International Conference on Robotics and Biomimetics:2399–2405

  41. Son J, Baek M, Cho M, Han B (2017) Multi-Object Tracking with Quadruplet Convolutional Neural Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5620–5629

  42. Stolkin R, Rees D, Talha M, Florescu I (2012) Bayesian fusion of thermal and visible spectra camera data for region based tracking with rapid background adaptation. In: IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems, pp 192–199

  43. Sundararaj V An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. International Journal of Intelligent Engineering and Systems 9(3):117–126

  44. Tang S, Andriluka M, Milan A, Schindler K, Roth S, Schiele B (2013) Learning people detectors for tracking in crowded scenes. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1049–1056

  45. Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library of computer vision algorithms. In: Proceedings of the international conference on multimedia, vol 1874249. ACM Firenze, Italy, pp 1469–1472

    Chapter  Google Scholar 

  46. Vicente TFY, Hoai M, Samaras D (2018) Leave-one-out kernel optimization for shadow detection and removal. IEEE Trans Pattern Anal Mach Intell 40(3):682–695

    Article  Google Scholar 

  47. VIVID Tracking Evaluation Web Site: http://vision.cse.psu.edu/data/vividEval/datasets/datasets.html. Accessed 04 April 2019

  48. Wang B, Wang G, Chan KL, Wang L (2017) Tracklet association by online target-specific metric learning and coherent dynamics estimation. IEEE Trans Pattern Anal Mach Intell 39:589–602

    Article  Google Scholar 

  49. Wen J, Wang J, Zhang Q (2017) Nearly optimal bounds for orthogonal least squares. IEEE Trans Signal Process 65(20):5347–5356

    Article  MathSciNet  MATH  Google Scholar 

  50. Yang L, Chen C, Wang H, Zhang B, Han J (2016) Adaptive multi-class correlation filters. In: Pacific rim conference on multimedia. Springer, Cham, pp 680–688

    Google Scholar 

  51. Yang M, Wu Y, Jia Y (2017) A hybrid data association framework for robust online multi-object tracking. IEEE Trans Image Process 26(12):5667–5679

    Article  MathSciNet  MATH  Google Scholar 

  52. Yao F, Sekmen A, Malkani M (2008) A novel method for real-time multiple moving targets detection from moving IR camera. In: International Conference on Pattern Recognition, pp 1–4

  53. Yilmaz A, Li X, Shah M (2004) Contour-based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans Pattern Anal Mach Intell 26(11):1531–1536

    Article  Google Scholar 

  54. Yoon JH, Yang M-H, Lim J, Yoon K-J (2015) “Bayesian multi-object tracking using motion context from multiple objects,” In: 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 33–40

  55. Yu F, Li W, Li Q, Liu Y, Shi X, Yan J (2016) Poi: Multiple object tracking with high performance detection and appearance feature. In: European Conference on Computer Vision, pp 36–42

  56. Zhang B, Li Z, Cao X, Ye Q, Chen C, Shen L, Perina A, Jill R (2017) Output constraint transfer for kernelized correlation filter in tracking. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47(4):693–703

    Article  Google Scholar 

  57. Zhang B, Luan S, Chen C, Han J, Wang W, Perina A, Shao L (2018) Latent constrained correlation filter. IEEE Trans Image Process 27(3):1038–1048

    Article  MathSciNet  MATH  Google Scholar 

  58. Sundararaj, V, Muthukumar, S, Kumar, RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288

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Authors and Affiliations

  1. Department of ECE, SSN College of engineering, Kalavakkam, Tamilnadu, India

    Anbuselvi Mathivanan

  2. Department of EEE, SSN College of engineering, Kalavakkam, Tamilnadu, India

    Saravanan Palaniswamy

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  1. Anbuselvi Mathivanan
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  2. Saravanan Palaniswamy
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Correspondence to Anbuselvi Mathivanan.

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Mathivanan, A., Palaniswamy, S. Efficient fuzzy feature matching and optimal feature points for multiple objects tracking in fixed and active camera models. Multimed Tools Appl 78, 27245–27270 (2019). https://doi.org/10.1007/s11042-019-07825-5

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