Dynamic Background Modeling and Foreground Detection using Orthogonal Projection onto the Subspace of Moving Objects
Pages 171 - 176
Abstract
Moving object detection and recognition is an important and very relevant topic for any video surveillance application. Object detection in video sequences is a challenging task due to several hindrances such as illumination variations, shadows, dynamic background, and clutter background. Existing methods are inadequate in addressing these challenges. Therefore, the author proposes a novel system for moving object detection in video based on Orthogonal Preserving Projection (OLPP). OLPP generates orthogonal basis functions which preserve locality better than Locality Preserving Projection (LPP), as it is non-orthogonal which makes it hard in reconstructing the data. Therefore, OLPP is deemed to pertain higher power of discrimination than LPP. The proposed method was tested on standard and the author's own dataset. The results obtained with the proposed system were compared with existing methods and the results are satisfactory. The proposed system is efficient and robust for detecting and recognizing moving objects in video surveillance applications.
References
[1]
S.A Robila. 2004. Independent Component Analysis. Springer, Berlin, Heidelberg. DOI https://doi.org/10.1007/978-3-662-05605-9_5
[2]
P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman. 1997. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), 711-720.
[3]
Jolliffe. 2002. Principal Component Analysis. Springer Science & Business Media.
[4]
Gitam Shikkenawis and Suman K. Mitra. 2012. Improving the Locality Preserving Projection for dimensionality reduction. 2012 Third International Conference on Emerging Applications of Information Technology (2012).
[5]
Fadi Dornaika and Ammar Assoum. 2013. Enhanced and parameterless Locality Preserving Projections for face recognition. Neurocomputing 99, (2013), 448-457.
[6]
Guoxian Yu, Hong Peng, Jia Wei, and Qianli Ma. 2011. Enhanced locality preserving projections using robust path based similarity. Neurocomputing 74, 4 (2011), 598-605.
[7]
D. Cai, X. He, J. Han, and H.-J. Zhang. 2006. Orthogonal Laplacianfaces for Face Recognition. IEEE Transactions on Image Processing 15, 11 (2006), 3608-3614.
[8]
Yi Huang, Dong Xu, and Feiping Nie. 2012. Semi-Supervised Dimension Reduction Using Trace Ratio Criterion. IEEE Transactions on Neural Networks and Learning Systems 23, 3 (2012), 519-526.
[9]
Lin, J. and Gunopulos, D., 2003, May. Dimensionality reduction by random projection and latent semantic indexing. In proceedings of the Text Mining Workshop, at the 3rd SIAM International Conference on Data Mining https://cs.gmu.edu/∼jessica/publications/lsi_sdm_workshop03.pdf.
[10]
Ghodsi, A. (2006). Dimensionality reduction a short tutorial. Department of Statistics and Actuarial Science, Univ. of Waterloo, Ontario, Canada, 37(38), 2006. https://people.computing.clemson.edu/∼ekp/courses/cpsc9500/assets/pca2.pdf
[11]
Ashutosh Saxena, Abhinav Gupta, and Amitabha Mukerjee. 2004. Non-linear Dimensionality Reduction by Locally Linear Isomaps. Neural Information Processing (2004), 1038-1043.
[12]
Mikhail Belkin and Partha Niyogi. 2003. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation 15, 6 (2003), 1373-1396.
[13]
Ayesha, S., Hanif, M.K., & Talib, R. (2020). Overview and comparative study of dimensionality reduction techniques for high dimensional data. Inf. Fusion, 59, 44-58 https://doi.org/10.1016/j.inffus.2020.01.005.
[14]
Timo Flesch, Keno Juechems, Tsvetomira Dumbalska, Andrew Saxe, and Christopher Summerfield. 2022. Orthogonal representations for robust context-dependent task performance in brains and neural networks. Neuron 110, 24 (2022), 4212-4219.
[15]
Md Khaled Hasan, Md. Shamim Ahsan, Abdullah-Al-Mamun, S. H. Shah Newaz, and Gyu Myoung Lee. 2021. Human Face Detection Techniques: A Comprehensive Review and Future Research Directions. Electronics 10, 19 (2021), 2354.
[16]
Jingxin Zhang, Maoyin Chen, Hao Chen, Xia Hong, and Donghua Zhou. 2019. Process Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation. Industrial & Engineering Chemistry Research 58, 14 (2019), 5579-5587.
[17]
ianyong Zhu, Jingwei Chen, Bin Xu, Hui Yang, and Feiping Nie. 2023. Fast orthogonal locality-preserving projections for unsupervised feature selection. Neurocomputing 531, (2023), 100-113.
[18]
Muhammad Zohaib Hassan Shah, Zahoor Ahmed, and Lisheng Hu. 2022. Feature extraction and fault detection scheme via improved locality preserving projection and SVDD. Transactions of the Institute of Measurement and Control 45, 2 (2022), 197-211.
[19]
W.K. Wong and H.T. Zhao. 2012. Supervised optimal locality preserving projection. Pattern Recognition 45, 1 (2012), 186-197.
[20]
Salifu Nanga, Ahmed Tijani Bawah, Benjamin Ansah Acquaye, Mac-Issaka Billa, Francis Delali Baeta, Nii Afotey Odai, Samuel Kwaku Obeng, and Ampem Darko Nsiah. 2021. Review of Dimension Reduction Methods. Journal of Data Analysis and Information Processing 9, 3 (2021), 189-231.
Recommendations
Moving Object Detection Using Three-Frame Difference and Background Subtraction
ICECC '12: Proceedings of the 2012 International Conference on Electronics, Communications and ControlMoving object detection is an important research content in video processing. Frames subtraction and background subtraction are commonly used methods to detect moving objects. They are both simple and effective, but have some limitations. Background ...
Multivalued Background/Foreground Separation for Moving Object Detection
WILF '09: Proceedings of the 8th International Workshop on Fuzzy Logic and ApplicationsThe detection of moving objects is usually approached by background subtraction, i.e. by constructing and maintaining an up-to-date model of the background and detecting moving objects as those that deviate from such a model. We adopt a previously ...
Illumination invariant foreground detection using multi-subspace learning
We propose a learning algorithm using multiple eigen subspaces to handle sudden illumination variations in background subtraction. The feature space is organized into clusters representing the different lighting conditions. A Local Principle Component ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
August 2023
783 pages
ISBN:9798400700224
DOI:10.1145/3607947
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 September 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
IC3 2023
IC3 2023: 2023 Fifteenth International Conference on Contemporary Computing
August 3 - 5, 2023
Noida, India
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 21Total Downloads
- Downloads (Last 12 months)10
- Downloads (Last 6 weeks)0
Reflects downloads up to 24 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format