Impact of Wolf Thresholding on Background Subtraction for Human Motion Detection
Submitted : 2024-02-12, Published : 2024-05-31.
Abstract
Series of motion detection based on background subtraction there is an image segmentation stage. Thresholding is a common technique used for the segmentation process. There are two types that can be used in thresholding techniques namely local and global. This research intends to implement local adaptive wolf thresholding as the threshold value of the background subtraction method to detect motion objects. The proposed method consists of the reading frame, background and foreground initialization of each frame, preprocessing, background subtraction, wolf thresholding, providing a bounding box, and running frame sequentially. Based on MSE and PSNR obtained on four videos, it has shown that wolf thresholding has succeeded in outperforming of global threshold.
Keywords
References
D. Das and S. Saharia, “Implementation and Performance Evaluation of Background Subtraction Algorithms,” International Journal on Computational Science & Applications, vol. 4, no. 2, pp. 49–55, 2014, doi: 10.5121/ijcsa.2014.4206
P. Bhuvaneswari and T. Siva Kumar, “Moving Object Tracking using Background Subtraction Technique and its Parametric Evaluation,” International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), vol. 3, no. 9, 2014.
L. Maddalena and A. Petrosino, “Background subtraction for moving object detection in RGBD data: A survey,” Journal of Imaging, vol. 4, no. 5. MDPI Multidisciplinary Digital Publishing Institute, 2018. doi: 10.3390/jimaging4050071.
S. Kumar and J. Sen Yadav, “Segmentation of moving objects using background subtraction method in complex environments,” Radioengineering, vol. 25, no. 2, pp. 399–408, Jun. 2016, doi: 10.13164/re.2016.0399
E. A. Pambudi, E. S. Wijaya, and A. Fauzan, “Improved Sauvola Threshold for Background Subtraction on Moving Object Detection,” International Journal of Software Engineering and Computer Systems, vol. 5, no. 2, pp. 78–89, 2019, doi: 10.15282/ijsecs.5.2.2019.6.0062.
N. Senthilkumaran and S. Vaithegi, “Image segmentation by using thresholding techniques for medical images,” Computer Science & Engineering: An International Journal, vol. 6, no. 1, pp. 1–13, 2016.
M. Chandrakala, “Comparative Study and Image Analysis of Local Adaptive Thresholding Techniques,” International Journal of Engineering Trends and Technology, vol. 35, no. 9, 2016, [Online]. Available: http://www.ijettjournal.org
A. Zidan, M. Abdelfatah, A. Fouad, A. E. Hassanien, and H. Hefny, “Wolf Local Thresholding Approach for Liver Image Segmentation in CT Images,” vol. 427, 2016, pp. 641–651. doi: 10.1007/978-3-319-29504-6_59.
S. Singh, A. Prasad, K. Srivastava, and S. Bhattacharya, “A Novel Method to Improve Basic Background Subtraction Methods for Object Detection in Video Surveillance System,” International Journal of Applied Engineering Research, vol. 13, no. 4, pp. 1866–1873, 2018, [Online]. Available: http://www.ripublication.com
Y. C. Beevi P and S. Natarajan, “An efficient Video Segmentation Algorithm with Real time Adaptive Threshold Technique,” International Journal of Signal Processing, vol. 2, no. 4, 2009.
Erwin and T. Yuningsih, “Detection of Blood Vessels in Optic Disc with Maximum Principal Curvature and Wolf Thresholding Algorithms for Vessel Segmentation and Prewitt Edge Detection and Circular Hough Transform for Optic Disc Detection,” Iranian Journal of Science and Technology - Transactions of Electrical Engineering, vol. 45, no. 2, pp. 435–446, Jun. 2021, doi: 10.1007/s40998-020-00367-9.
R. M. Pinki, “Estimation of the Image Quality under Different Distortions,” International Journal Of Engineering And Computer Science, vol. 5, no. 17291, pp. 17291–17296, 2016, doi: 10.18535/ijecs/v5i7.20.
C. Wolf and J.-M. Jolion, “Extraction and recognition ofartificial text in multimedia documents,” Formal Pattern Analysis &Applications, vol. 6, no. 4, pp. 309–326, 2004, doi: 10.1007/s10044-003-0197-7.
Article Metrics
Abstract view: 106 times
Download : 60 times
Download : 20 times
This work is licensed under a Creative Commons Attribution 4.0 International License.
Refbacks
- There are currently no refbacks.