Published August 30, 2023 | Version CC BY-NC-ND 4.0
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Enhancing Occlusion Handling in Real-Time Tracking Systems through Geometric Mapping and 3D Reconstruction Validation

Creators

  • 1. Department of Information Technology, Rajalakshmi Engineering College, Chennai (Tamil Nadu), India.
  • 2. Department of Computer Science and Business Systems, Rajalakshmi Engineering College, Chennai (Tamil Nadu), India.
  • 3. Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai (Tamil Nadu), India.

Contributors

Contact person:

  • 1. Department of Information Technology, Rajalakshmi Engineering College, Chennai (Tamil Nadu), India.

Description

Abstract: Object detection is a classic research problem in the area of Computer Vision. Many smart world applications, like, video surveillance or autonomous navigation systems require a high accuracy in pose detection of objects. One of the main challenges in Object detection is the problem of detecting occluded objects and its respective 3D reconstruction. The focus of this paper is inter-object occlusion where two or more objects being tracked occlude each other. A novel algorithm has been proposed for handling object occlusion by using the technique of geometric matching and its 3D projection obtained. The developed algorithm has been tested using sample data and the results are presented.

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Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

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Journal article: 2249-8958 (ISSN)

References

  • D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2004 https://doi.org/10.1023/B:VISI.0000029664.99615.94
  • A. Toshev, B. Taskar, and K. Daniilidis. Object detection via boundary structure segmentation. In CVPR, 2010. https://doi.org/10.1109/CVPR.2010.5540114
  • S. Hinterstoisser, V. Lepetit, S. Ilic, P. Fua, and N. Navab. Dominant orientation templates for real-time detection of texture-less objects.In CVPR, 2010. https://doi.org/10.1109/CVPR.2010.5539908
  • T. Gao, B. Packer, and D. Koller. A segmentation-aware object detection model with occlusion handling. In CVPR, 2011. https://doi.org/10.1109/CVPR.2011.5995623
  • HimanshuChandelBahra University Waknghat, Shimla.,SoniaVatta" Occlusion Detection and Handling: A Review" International Journal of Computer Applications (0975 – 8887) Volume 120 – No.10, June 2015. https://doi.org/10.5120/21264-3857
  • Eshed Ohn-Bar, Member, IEEE, and Mohan Manubhai Trivedi, Fellow, IEEE" Learning to Detect Vehicles by Clustering Appearance Patterns" To Appear in IEEE Transactions on Intelligent Transportation Systems 2015 https://doi.org/10.1109/TITS.2015.2409889
  • Tao Yang a, Yanning Zhang a, Rui Yub, Xiaoqiang Zhang a, Ting Chena, Lingyan Ran a,Zhengxi Song a, WenguangMa" Simultaneous active camera array focus plane estimation and occluded moving object imaging" Image and Vision Computing 32 (2014) 510–521 https://doi.org/10.1016/j.imavis.2014.05.001
  • L.Priya and Sheila Anand "Shape Based Object Detection for Partially Occluded Objects Under Front Lighting Techniques" 2015 IEEE European Modeling Symposium, 978-1-5090-0206-1/15 2015 IEEE DOI 10.1109/EMS.2015.27
  • Priya L, Anand S. Object recognition and 3D reconstruction of occluded objects using binocular stereo. Cluster Computing. 2018 Mar;21:29-38. https://doi.org/10.1007/s10586-017-0891-7
  • Anand, S., Priya, L. (2019). A Guide for Machine Vision in Quality Control. United States: CRC Press. https://doi.org/10.1201/9781003002826
  • R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision. New York, NY: McGraw-Hill, 1995.
  • Shreyas Fadnavis 2014, 'Image Interpolation Techniques in DigitalImage Processing', An Overview Int. Journal of Engineering Researchand Applications www.ijera.com ISSN : 2248-9622, vol. 4, issue 10,pp.70-73.
  • E. Ohn-Bar and M. M. Trivedi, "Fast detection of multiple objects in traffic scenes," IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 644-660, 2012.
  • Eshed Ohn-Bar and Mohan M. Trivedi Fast and Robust Object Detection Using Visual Subcategories Computer Vision and Robotics Research Laboratory University of California, San Diego 2020
  • S. Sivaraman and M. M. Trivedi, "Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis," in IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1773-1795, Dec. 2013, doi: 10.1109/TITS.2013.2266661. https://doi.org/10.1109/TITS.2013.2266661
  • T. Yang et al., "Automatic focus and occluded moving object tracking using SIFT and geometric projection," J. Real-Time Image Process., vol. 12, no. 1, pp. 55-70, 2016.
  • Fei Chen, Xiaodong Wang, Yunxiang Zhao, Shaohe Lv, Xin Niu,Visual object tracking: A survey,Computer Vision and Image Understanding,Volume 222,2022,103508,ISSN 1077-3142,https://doi.org/10.1016/j.cviu.2022.103508. https://doi.org/10.1016/j.cviu.2022.103508
  • K. R. Castleman, Digital Image Processing. Upper Saddle River, NJ: Prentice Hall, 1996. external-identifier:"urn:lcp:digitalimageproc0000cast:epub:786d717c-ea93-4a36-9c8b-ea041763a2dc"

Subjects

ISSN: 2249-8958 (Online)
https://portal.issn.org/resource/ISSN/2249-8958#
Retrieval Number:100.1/ijeat.F42590812623
https://www.ijeat.org/portfolio-item/F42590812623/
Journal Website: www.ijeat.org
https://www.ijeat.org/
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
https://www.blueeyesintelligence.org/