CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Review on Object Tracking Across Real-World Multi Camera Environment

by Rino Cherian, Jothimani K., Reeja S.R.
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 12
Year of Publication: 2021
Authors: Rino Cherian, Jothimani K., Reeja S.R.
10.5120/ijca2021921007

Rino Cherian, Jothimani K., Reeja S.R. . A Review on Object Tracking Across Real-World Multi Camera Environment. International Journal of Computer Applications. 174, 12 ( Jan 2021), 32-37. DOI=10.5120/ijca2021921007

@article{ 10.5120/ijca2021921007,
author = { Rino Cherian, Jothimani K., Reeja S.R. },
title = { A Review on Object Tracking Across Real-World Multi Camera Environment },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2021 },
volume = { 174 },
number = { 12 },
month = { Jan },
year = { 2021 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number12/31733-2021921007/ },
doi = { 10.5120/ijca2021921007 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:23:46.930754+05:30
%A Rino Cherian
%A Jothimani K.
%A Reeja S.R.
%T A Review on Object Tracking Across Real-World Multi Camera Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 12
%P 32-37
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Object identification, tracking and monitoring are significant and testing assignments in numerous computer vision applications, for example, surveillance, vehicle navigation and self-governing robot navigation. Video reconnaissance in a powerful environment, particularly for people and vehicles, is one of the momentum testing research points. It is a key innovation to battle against illegal activity, offence, public security and for effective administration of traffic. The work includes planning of the proficient video observation framework in complex conditions. In video observation, recognition of moving items from a video is significant for object discovery, target tracking, and behavior understanding. Recognition of moving items in video transfers is the primary significant advance of data and background subtraction is a famous methodology for frontal segmentation. In this paper, after surveying ongoing advances of online article, we do enormous scope tries different things with different assessment standards to see how these algorithms perform. By breaking down quantitative outcomes, we distinguish viable methodologies for powerful following and give potential future examination bearings in this field.

References
  1. Kim, Dae Ha, et al. "Real-time purchase behavior recognition system based on deep learning-based object detection and tracking for an unmanned product cabinet”, International Journal of Expert Systems with Applications, Elsevier, Volume 143, 1 April 2020, 11306
  2. Rani, Geeta, and Anita Jindal. "Real-Time Object Detection and Tracking Using Velocity Control," Smart Systems and IoT: Innovations in Computing, Springer, Singapore, 2020. 767-778.
  3. Haalck, Lars, et al. "Towards image-based animal tracking in natural environments using a freely moving camera." Journal of neuroscience methods Elsevier, Volume 330 (2020): 108455.
  4. Singh, Surender, et al. "Object Motion Detection Methods for Real-Time Video Surveillance: A Survey with Empirical Evaluation." Smart Systems and IoT: Innovations in Computing, Springer, Singapore, 2020. 663-679.
  5. Zhang, Hui, et al. "Object Tracking in RGB-T Videos Using Modal-Aware Attention Network and Competitive Learning." Sensors 20.2 (2020): 393.
  6. Jung, Sukwoo, et al. "Moving object detection from moving camera image sequences using an inertial measurement unit sensor." Applied Sciences 10.1 (2020): 268.
  7. Lin, Hsien-I., Zhangguo Yu, and Yi-Chen Huang. "Ball Tracking and Trajectory Prediction for Table-Tennis Robots", Sensors 20.2 (2020): 333.
  8. Gong, Zheng, et al. "A Frustum-based probabilistic framework for 3D object detection by fusion of LiDAR and camera data." ISPRS Journal of Photogrammetry and Remote Sensing . Elsevier, Volume 159 (2020): 90-100.
  9. Evans M, Osborne CJ, Ferryman JM (2013) Multicamera object detection and tracking with object size estimation. In: AVSS, IEEE, pp 177–182
  10. Youlu W (2013) Distributed multi-object tracking with multi-camera systems composed of overlapping and non-overlapping cameras,PhD thesis, University of NebraskaLincoln.
  11. Iguernaissi, Rabah, et al. "People tracking in multi-camera systems: a review." Multimedia Tools and Applications, springer, 78.8 (2019): 10773-10793.
  12. del Blanco CR, Mohedano R, Garca NN, Salgado L, Jaureguizar F (2008) Color-based 3d particle filtering for robust tracking in heterogeneous environments. In: ICDSC, IEEE, pp 1–10
  13. Fleuret F, Berclaz J, Lengagne R, Fua P (2008) Multicamera people tracking with a probabilistic occupancy map. Pattern Analysis and Machine Intelligence, IEEE Transactions on 30(2):267–282
  14. Huang C, Wang SJ (2012) A bayesian hierarchical framework for multitarget labeling and correspondence with ghost suppression over multicamera surveillance system. IEEE T Automation Science and Engineering 9(1):16–30
  15. Khan SM, Shah M (2006) A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: Leonardis A, Bischof H, Pinz A (eds) ECCV (4), Springer, Lecture Notes in Computer Science, vol 3954, pp 133–146
  16. Kim K, Davis LS (2006) Multi-camera tracking and segmentation of occluded people on ground plane using search- guided particle filtering. In: In ECCV, pp 98–109
  17. Santos TT, Morimoto CH (2011) Multiple camera people detection and tracking using support integration. Pattern Recogn Lett 32(1):47–55, DOI 10.1016/j.patrec.2010.05.016, URL http://dx.doi.org/10.1016/j.patrec.2010.05.016
  18. Khan SM, Shah M (2006) A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: Leonardis A, Bischof H, Pinz A (eds) ECCV (4), Springer, Lecture Notes in Computer Science, vol 3954, pp 133–146
  19. Liem MC, Gavrila DM (2014) Joint multi-person detection and tracking from overlapping cameras. Computer Vision and Image Understanding 128:36–50
  20. Chen W, Cao L, Chen X, Huang K (2016) An equalized global graph model-based approach for multi-camera object tracking. IEEE Transactions on Circuits and Systems for Video Technology
  21. Arsic D, Hristov E, Lehment NH, Hrnler B, Schuller B, Rigoll G (2008) Applying multi layer homography for multi camera person tracking. In: ICDSC, IEEE, pp 1–9
  22. Kuo CH, Huang C, Nevatia R (2010) Multi-target tracking by on-line learned discriminative appearance models. In: CVPR, IEEE, pp 685–692
  23. Zheng WS, Gong S, Xiang T (2013) Reidentification by relative distance comparison. IEEE Trans Pattern Anal Mach Intell 35(3):653–668
  24. Nie W, Liu A, Su Y, Luan H, Yang Z, Cao L, Ji R (2014) Single/cross-camera multipleperson tracking by graph matching. Neurocomputing 139:220–232
  25. Lin Z, Davis LS (2008) Learning pairwise dissimilarity profiles for appearance recognition in visual surveillance. In: Bebis G, Boyle RD, Parvin B, Koracin D, Remagnino P, Porikli FM, Peters J, Klosowski JT, Arns LL, Chun YK, Rhyne TM, Monroe L (eds) ISVC (1), Springer, Lecture Notes in Computer Science, vol 5358, pp 23–34
  26. Mazzon R, Cavallaro A (2013) Multi-camera tracking using a multi-goal social force model. Neurocomputing 100:41–50 61.
  27. Mazzon R, Tahir SF, Cavallaro A (2012) Person re-identification in crowd. Pattern Recognition Letters 33(14):1828–1837 62.
  28. Medeiros H, Park J, Kak A (2008) Distributed object tracking using a cluster-based kalman filter in wireless camera networks. Selected Topics in Signal Processing, IEEE Journal of 2(4):448–463, DOI 10.1109/JSTSP.2008.2001310
  29. Javed O, Shafique K, Rasheed Z, Shah M (2008) Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views. Computer Vision
  30. Hamdoun O, Moutarde F, Stanciulescu B, Steux B (2008) Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: ICDSC, IEEE, pp 1–6
  31. Bouma H, Borsboom S, den Hollander RJ, Landsmeer SH, Worring M (2012) Reidentification of persons in multi-camera surveillance under varying viewpoints and illumination. In: SPIE Defense, Security, and Sensing, International Society for Optics and Photonics, pp 83,590Q–83,590Q
  32. Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person reidentification by symmetry-driven accumulation of local features. In: CVPR, IEEE, pp 2360–2367
  33. Chen W, Cao L, Chen X, Huang K (2014) A novel solution for multi-camera object tracking. In: 2014 IEEE International Conference on Image Processing (ICIP), IEEE, pp 2329–2333
  34. Chen W, Cao L, Chen X, Huang K (2016) An equalized global graph model-based approach for multi-camera object tracking. IEEE Transactions on Circuits and Systems for Video Technology 22.
  35. Chen Y, Zhu X, Gong S, et al (2018) Person re-identification by deep learning multiscale representations 24. Cong DNT, Khoudour L, Achard C, Meurie C, Lezoray O (2010) People reidentification by spectral classification of silhouettes. Signal Processing 90(8):2362– 2374
  36. Kenk VS, Kovaciˇ c S, Kristan M, Hajdinjak M, Per ˇ s J, et al (2015) Visual re- ˇ identification across large, distributed camera networks. Image and Vision Computing 34:11–26
  37. Nakajima C, Pontil M, Heisele B, Poggio T (2003) Full-body person recognition system. Pattern Recognition 36(9):1997–2006
  38. S. R. Reeja and N. P. Kavya, "Real time video denoising," 2012 IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA), Kottayam, 2012, pp. 1-5, doi: 10.1109/AICERA.2012.6306745.
  39. Reeja S.R and N P Kavya. Article: Noise Reduction in Video Sequences The State of Art and the Technique for Motion Detection. International Journal of Computer Applications 58(8):31-36, November 2012
  40. Reeja S.R and N P Kavya, Noise Reduction in Video Sequences – The State of Art and the Technique for Motion Detection, International Journal of Computer Applications (0975 – 8887) Volume 58– No.8, November 2012
  41. Dias, Norman & Reeja. (2018). A quantitative report on the present strategies of Graphical authentication. International Journal of Computer Sciences and Engineering. 06. 64-73. 10.26438/ijcse/v6si10.6473. 12.
  42. Reeja S R, Kumar Abhishek Gaurav, Ladly Patel, Rino Cherian, (2018)“Garbage Management Using Internet of Things”, International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.56-59, 2018. 13.
  43. Reeja S R, Venkat Durga Sriram, Tarun Reddy R, Venkatamanu, Rino Cherian,(2018) "Ultrasonic Distance Measurement", International Journal of Computer Sciences and Engineering, Vol.06, Special Issue.10, pp.42-44, 2018. 14.
  44. Kaveri Hiremath, Dr. Reeja S. R, 2017, A Survey on Self Adjusting Slots & Dynamic Job Ordering for Mapreduce Workloads using Homogeneous and Heterogeneous Hadoop Cluster, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) ICPCN – 2017 (Volume 5 – Issue 19)
  45. Kavya, Reeja S. R, Dr NP. (2014), "An Approach for Noise Removal from a Sequence of Video." International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April2014, pg.1266-1270, ISSN 2229-5518
  46. Reeja, S. R. (2014) "An Automated System for Detecting Congestion in Huge Gatherings.", International Journal of Computer Applications (0975 – 8887) International Conference on Information and Communication Technologies (ICICT-2014) 17.
  47. Reeja, S. R, Rino cherian,Dr Jothimani k, “PIXEL LEVEL REVERSIBLE DATA HIDING USING TWO LEVEL ENCRYPTIONS REVIEW”, International Journal of Engineering Applied Sciences and Technology, 2019 Vol. 4, Issue 5, ISSN No. 2455-2143, Pages 498-503.
  48. Dr Reeja S R , Mr. Murthuza, Mr. Rino Cherian, Dr Jothimani, “A survey and development on context-aware monitoring in healthcare with big data”, International Journal of Big Data Intelligence, 2020 Vol.7 No.2, pp.97 – 109
  49. Kiran J Waghmare, Dr Reeja S R, “A Computational Intelligence Paradigm with Human Computer Interface Learning”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume 9, Issue 2s, Page 384-389.
  50. Dias N., Reeja S.R. (2020) An Improvement of Compelling Graphical Confirmation Plan and Cryptography for Upgrading the Information Security and Preventing Shoulder Surfing Assault. In: Arai K., Bhatia R., Kapoor S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1070. Springer, Cham
  51. Soja Rani S., Reeja S.R. (2020) A Survey on Different Approaches for Malware Detection Using Machine Learning Techniques. In: Karrupusamy P., Chen J., Shi Y. (eds) Sustainable Communication Networks and Application. ICSCN 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 39. Springer, Cham.
  52. Dr Reeja S R, NP Kavya, “A System for Movement Detecting Congestion” , INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, Vol.13, No.4.
Index Terms

Computer Science
Information Sciences

Keywords

Object Tracking

Powered by PhDFocusTM