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Analysis of Target Detection and Tracking for Intelligent Vision System

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Deep Learning and Big Data for Intelligent Transportation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 945))

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Abstract

In the present scenario, most of the researchers are highly motivated to do new findings in the field of computer vision because of its tremendous applications such as video surveillance, human-machine interaction, traffic monitoring, human behavioral analysis, the guided missile, and military services, medical applications, and vehicle navigation. A typical video data frame comprises both foregrounds as well as background information. The pixel points that describe the target features in the region of interest are considered foreground information, and the rest of the feature points are treated as background information. Moving object detection plays a pivotal role in any kind of computer vision applications. Moving object detection is the process of identifying the class objects such as people, vehicles, toys, and human faces in the video sequences more precisely without background disturbances. In most cases, the existing moving object detection approaches concentrate only on the foreground information and frequently ignore the background information. As a result, trackers will be deviated away from the target and detect the non-foreground objects. Recently, several contributions have been proposed for moving object detection. However, the robustness and novelty are still challenging to achieve because of the complex environments, including illumination changes, rapid variations in target appearance, similar objects in the background, occlusions, target rotations, scaling, fast and abrupt motion changes, moving soft shadow, flat surface regions, and dynamic backgrounds. This book chapter introduces the concept of an intelligent video surveillance system and the major challenges involved in moving object detection. This chapter also deliberates the related backgrounds and their shortcomings in different perspectives. It also presents potential algorithms for efficient object detection and tracking. Finally, the book chapter is concluded with research opportunities pertaining to object detection and tracking.

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References

  1. http://cvlab.hanyang.ac.kr/tracker_bench

  2. http://www4.comp.polyu.edu.hk/~cslzhang/FCT/FCT.html

  3. http://perception.i2r.a-star.edu.sg/bk_model/bk_index.html

  4. Rout RK (2013) A survey on object detection and tracking algorithms

    Google Scholar 

  5. Zhang LZL, Liang YLY (2010) Motion human detection based on background subtraction. In: 2010 Second international workshop on education technology and computer science (ETCS), vol 1, pp 284–287

    Google Scholar 

  6. Lo BPL, Velastin SA (2001) Automatic congestion detection system for underground platforms. In: Proceedings of 2001 international symposium on intelligent multimedia, video and speech processing, ISIMP 2001 (IEEE Cat. No.01EX489), pp 158–161

    Google Scholar 

  7. Parks DH, Fels SS (2008) Evaluation of background subtraction algorithms with post-processing. In: Proceedings—IEEE 5th international conference on advanced video and signal based surveillance, AVSS 2008, pp 192–199

    Google Scholar 

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

    Article  Google Scholar 

  9. Kinoshita K, Enokidani M, Izumida M, Murakami K (2006) Tracking of a moving object using one-dimensional optical flow with a rotating observer. In: 2006 9th International conference on control, automation, robotics and vision

    Google Scholar 

  10. Bowyer K (2001) Edge detector evaluation using empirical ROC curves. Comput Vis Image Underst 84(1):77–103

    Article  MathSciNet  Google Scholar 

  11. Deori B, Thounaojam DM (2014) A survey on moving object tracking in video. Int J Inf Theor (IJIT) 3(3):31–46

    Google Scholar 

  12. Yang H, Shao L, Zheng F, Wang L, Song Z (2011) Recent advances and trends in visual tracking: a review. Neurocomputing 74(18):3823–3831

    Article  Google Scholar 

  13. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–43

    Article  Google Scholar 

  14. Veenman CJ, Reinders MJ, Backer E (2001) Resolving motion correspondence for densely moving points. IEEE Trans Pattern Anal Mach Intell 23(1):54–72

    Article  Google Scholar 

  15. Serby D, Gool LV (2004) Probabilistic object tracking using multiple features. In: Proceedings of 17th International conference on the pattern recognition (ICPR’04) vol 2, pp 184–187

    Google Scholar 

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

    Article  Google Scholar 

  17. Li, X, Hu, W, Shen, C, Zhang, Z, Dick, A, Hengel, AVD (2013) A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 4(4):58:51–58:48

    Google Scholar 

  18. Bhaskar H, Dwivedi K, Dogra DP, Al-Mualla M, Mihaylova L (2015) Autonomous detection and tracking under illumination changes, occlusions and moving camera. Signal Processing 117:343–354

    Google Scholar 

  19. Barnich O, Droogenbroeck MV (2011) ViBe: a universal background subtraction algorithm for video sequences ViBe: a universal background subtraction algorithm for video sequences. IEE Trans Image Process 20(6):1709–1724

    Article  Google Scholar 

  20. Sheikh Y, Javed O, Kanade T (2009) ‘Background subtraction for freely moving cameras. In: Proceedings of IEEE 12th international conference on computer vision, pp 1219–1225

    Google Scholar 

  21. Jansari D, Parmar S (2013) Novel object detection method based on optical flow. In: Proceedings international conference on emerging trends in computer and image processing, Malaysia, pp 197–201, 8–9 Jan 2013

    Google Scholar 

  22. Aslani S, Mahdavi-Nasab H (2013) Optical flow based moving object detection and tracking for traffic surveillance. Int J Electr Comput Energ Electron Commun Eng 7(9):963–967

    Google Scholar 

  23. Liao S, Zhao G, Kellokumpu V, Pietikäinen M, Li SZ (2010) Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 1301–1306

    Google Scholar 

  24. Lee Y, Jung J, Kweon I-S (2011) Hierarchical on-line boosting based background subtraction. In: Proceedings of 17th Korea-Japan joint workshop on frontiers of computer vision (FCV), pp 1–5

    Google Scholar 

  25. Zhang Z, Wang C, Xiao B, Liu S, Zhou W (2012) Multi-scale fusion of texture and color for background modeling. In: Proceedings—2012 IEEE 9th international conference on advanced video and signal-based surveillance, AVSS 2012, pp 154–159

    Google Scholar 

  26. Ma F, Sang N (2013) Background subtraction based on multi-channel SILTP. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 7728 LNCS, no PART 1, pp 73–84

    Google Scholar 

  27. Han H, Zhu J, Liao S, Lei Z, Li SZ (2015) Moving object detection revisited: speed and robustness. IEEE Trans Circ Syst Video Technol 25(6):910–921

    Article  Google Scholar 

  28. Narayana M, Hanson A, Learned-Miller E (2012) Background modeling using adaptive pixelwise kernel variances in a hybrid feature space, pp 2104–2111

    Google Scholar 

  29. Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632

    Article  Google Scholar 

  30. Mei X, Ling H (2011) Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell 33(11):2259–2272

    Article  Google Scholar 

  31. Zhang K, Zhang L, Yang M-H (2014) Fast compressive tracking. IEEE Trans Pattern Anal Mach Intell 36(10):2002–2015

    Article  Google Scholar 

  32. Fan B, Du Y, Cong Y (2014) Online learning discriminative dictionary with label information for robust object tracking. In: Abstract and applied analysis, vol 2014

    Google Scholar 

  33. Liao P-S, Chen T-S, Chung P-C (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 17(5):713–727

    Google Scholar 

  34. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874

    Article  Google Scholar 

  35. Powers DMW (2011) Evaluation: from precision, recall and F-factor to ROC, informedness, markedness & correlation

    Google Scholar 

  36. http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html

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Author information

Authors and Affiliations

  1. KPR Institute of Engineering and Technology, Arasur, Coimbatore, Tamil Nadu, India

    K. Kalirajan, D. Venugopal & V. Seethalakshmi

  2. SNS College of Engineering, Coimbatore, Tamil Nadu, India

    K. Balaji

Authors
  1. K. Kalirajan
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  2. K. Balaji
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  3. D. Venugopal
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  4. V. Seethalakshmi
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Corresponding author

Correspondence to K. Kalirajan .

Editor information

Editors and Affiliations

  1. School of Computing, Southern Illinois University, Carbondale, IL, USA

    Khaled R. Ahmed

  2. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Kalirajan, K., Balaji, K., Venugopal, D., Seethalakshmi, V. (2021). Analysis of Target Detection and Tracking for Intelligent Vision System. In: Ahmed, K.R., Hassanien, A.E. (eds) Deep Learning and Big Data for Intelligent Transportation. Studies in Computational Intelligence, vol 945. Springer, Cham. https://doi.org/10.1007/978-3-030-65661-4_3

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