Object Detection and Tracking using Watershed Segmentation and KLT Tracker

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Tunirani Nayak
Tunirani Nayak
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Nilamani Bhoi
Nilamani Bhoi

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Object Detection and Tracking using Watershed Segmentation and KLT Tracker

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Abstract

In this paper, a moving object is extracted from a video using video object detection algorithm based on spatial and temporal segmentation. The technique begins with temporal segmentation in which edge map is extracted using edge operator. The initial binary mask is obtained by using morphological operation applied on initial edge map. The next phase is spatial segmentation where gradient image is obtained by multi-scale morphological operator. The modified gradient image is obtained by the operator applied over the current frame. At last, moving object is extracted by precisely and accurately by watershed segmentation which is performed on the modified gradient image. Again, morphological operation is applied on the output to get final binary mask. This binary mask is then complemented to yield the contour line of the video object. Using the binary mask, the video object is extracted from the video frames. After detection of video object, the object tracking is performed using Kanade-Lucas-Tomasi (KLT) feature tracker.

References

16 Cites in Article
  1. T Sikora (1997). The MPEG-4 video standard verification model.
  2. King Ngi,Ngan,Hongliang Li (2011). Video segmentation and its applications.
  3. A Neri,S Colonnese,G Russo,P Talone (1998). Automatic moving object and background separation.
  4. S Sharma (2013). Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc N/W.
  5. Renjie Li,Songyu Yu,Xiaokang Yang (2007). Efficient Spatio-temporal Segmentation for Extracting Moving Objects in Video Sequences.
  6. D Chinchkhede,N Uke (2002). Fast and Automatic Video Object Segmentation and Tracking for Content-Based Application.
  7. S Ganesan,Jalla (2009). Video Object Extraction Based on a Comparative Study of Efficient Edge Detection Technique International Arab.
  8. Gao Hai,Siu Wan2chi,Hou Chao2huan (2001). Improved techniques for automatic image segmentation.
  9. M Muthukrishnan,Radha (2011). Unknown Title.
  10. D Wang (1998). Unsupervised video segmentation based on watersheds and temporal tracking.
  11. John Canny (1986). A Computational Approach to Edge Detection.
  12. L Vincent,P Soille (1991). Watersheds in digital spaces: an efficient algorithm based on immersion simulations.
  13. Thomas Sikora (1997). The MPEG-4 video standard verification model.
  14. R Gonzalez,R Woods (2002). Randomly Generated Algorithms and Dynamic Connections.
  15. Hanqing Jiang,Guofeng Zhang,Huiyan Wang,Hujun Bao (2015). Spatio-Temporal Video Segmentation of Static Scenes and Its Applications.
  16. Nishu Singla (2014). Motion Detection Based on Frame Difference Method International Journal of.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Tunirani Nayak. 2020. \u201cObject Detection and Tracking using Watershed Segmentation and KLT Tracker\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 20 (GJCST Volume 20 Issue F1): .

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Issue Cover
GJCST Volume 20 Issue F1
Pg. 25- 32
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-F Classification: I.4.8
Version of record

v1.2

Issue date

August 25, 2020

Language
en
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In this paper, a moving object is extracted from a video using video object detection algorithm based on spatial and temporal segmentation. The technique begins with temporal segmentation in which edge map is extracted using edge operator. The initial binary mask is obtained by using morphological operation applied on initial edge map. The next phase is spatial segmentation where gradient image is obtained by multi-scale morphological operator. The modified gradient image is obtained by the operator applied over the current frame. At last, moving object is extracted by precisely and accurately by watershed segmentation which is performed on the modified gradient image. Again, morphological operation is applied on the output to get final binary mask. This binary mask is then complemented to yield the contour line of the video object. Using the binary mask, the video object is extracted from the video frames. After detection of video object, the object tracking is performed using Kanade-Lucas-Tomasi (KLT) feature tracker.

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Object Detection and Tracking using Watershed Segmentation and KLT Tracker

Tunirani Nayak
Tunirani Nayak
Nilamani Bhoi
Nilamani Bhoi

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