Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Intelligent Technique for Moving Object Detection from Problematic Video Captured through Camera Sensor

Author(s): Sneha Mishra and Dileep Kumar Yadav*

Volume 17, Issue 2, 2024

Published on: 24 July, 2023

Page: [107 - 115] Pages: 9

DOI: 10.2174/2215083810666230510113140

Price: $65

conference banner
Abstract

Aims: The significant aim of the proposed work is to develop an adaptive method to compute the threshold during run-time and update it adaptively for each pixel in the testing phase. It classifies motion-oriented pixels from the scene for moving objects using background subtraction and enhances using post-processing.

Background: According to the huge demand for surveillance system, society is looking towards an intelligent video surveillance system that detect and track moving objects from video captured through a surveillance camera. So, it is very crucial and highly recommended throughout the globe in numerous domains such as video-based surveillance, healthcare, transportation, and many more. Practically, this research area faces lots of challenging issues such as illumination variation, cluttered background, camouflage, etc. So, this paper has developed an adaptive background subtraction method to handle such challenging problems.

Objective: To focus and study the problematic video data captured through the camera sensor.

To handle challenging issues available in real-time video scenes.

To develop a background subtraction method and update the background model adaptively for moving object detection.

Methods: The proposed method has been accomplished using the following sections:

Background model construction

Automatic generation of threshold

Background subtraction

Maintenance of background model

Results: The qualitative analysis of the proposed work is experimented with publicly available datasets and compared with considered state-of-the-art methods. In this work, library sequence (thermal data) of CDNET and other color video frame sequences Foreground aperture, Waving Tree and Camouflage are considered from Microsoft’s Wallflower. The quantitative values depicted in Table- 1. This work demonstrate the better performance of the proposed method as compared to state-ofthe- art methods. It also generates better outcomes and handles the problem of a dynamic environment and illumination variation.

Conclusion: Currently, the world is demanding computer vision-based security and surveillancebased applications for society. This work has provided a method for the detection of moving information using an adaptive method of background subtraction approach for moving object detection in video scenes. The performance evaluation depicts better average results as compared to considered peer methods.

Next »
Graphical Abstract

[1]
M. Yazdi, and T. Bouwmans, "New trends on moving object detection in video images captured by a moving camera: A survey", Comput. Sci. Rev., vol. 28, pp. 157-177, 2018.
[http://dx.doi.org/10.1016/j.cosrev.2018.03.001]
[2]
D.K. Yadav, and K. Singh, "Adaptive background modelling technique for moving object detection in video under dynamic environment", Intern. J. Spatio-Temp. Data Sci., vol. 1, no. 1, pp. 4-21, 2019.
[http://dx.doi.org/10.1504/IJSTDS.2019.097600]
[3]
T. Bouwmans, A. Sobral, S. Javed, S.K. Jung, and E.H. Zahzah, "Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset", Comput. Sci. Rev., vol. 23, pp. 1-71, 2017.
[http://dx.doi.org/10.1016/j.cosrev.2016.11.001]
[4]
P. Chen, Y. Dang, R. Liang, W. Zhu, and X. He, "Real-time object tracking on a drone with multi-inertial sensing data", IEEE Trans. Intell. Transp. Syst., vol. 19, no. 1, pp. 131-139, 2018.
[http://dx.doi.org/10.1109/TITS.2017.2750091]
[5]
Q. Zeng, J. Adu, J. Liu, J. Yang, Y. Xu, and M. Gong, "Real-time adaptive visible and infrared image registration based on morphological gradient and C_SIFT", J. Real-Time Image Process., vol. 17, no. 5, pp. 1103-1115, 2020.
[http://dx.doi.org/10.1007/s11554-019-00858-x]
[6]
D.K. Yadav, "“Chapter-12: Detection of Moving Human in Vision based Smart Surveillance under Cluttered Background: An Application for IoT”, Book- Visual Surveillance to Internet of Things", Technology and Applications, Taylor & Francis, no. March, pp. 1-296, 2019.
[7]
M. Hashemi, and M. Hall, "Detecting and classifying online dark visual propaganda", Image Vis. Comput., vol. 89, pp. 95-105, 2019.
[http://dx.doi.org/10.1016/j.imavis.2019.06.001]
[8]
J. Song, B. Gao, W.L. Woo, and G.Y. Tian, "Ensemble tensor decomposition for infrared thermography cracks detection system", Infrared Phys. Technol., vol. 105, p. 103203, 2020.
[http://dx.doi.org/10.1016/j.infrared.2020.103203]
[9]
N. Goyette, P.M. Jodoin, F. Porikli, and P. Ishwar, "changedetection.net. A new change detection benchmark database",. In Proc. IEEE Workshop on Change Detection at CVPR, 2012, pp. 1-8
[10]
J. Ahmad, A. Akula, R. Mulaveesala, and H.K. Sardana, "An independent component analysis based approach for frequency modulated thermal wave imaging for subsurface defect detection in steel sample", Infrared Phys. Technol., vol. 98, pp. 45-54, 2019.
[http://dx.doi.org/10.1016/j.infrared.2019.02.006]
[11]
T. Bouwmans, F. Porikli, B. Höferlin, and A. Vacavant, Background Modeling and Foreground Detection for Video Surveillance., Chapman & Hall: London, U.K., 2014.
[http://dx.doi.org/10.1201/b17223]
[12]
A. Sanin, C. Sanderson, and B.C. Lovell, "Shadow detection: A survey and comparative evaluation of recent methods", Pattern Recognit., vol. 45, no. 4, pp. 1684-1695, 2012.
[http://dx.doi.org/10.1016/j.patcog.2011.10.001]
[13]
T.S.F. Haines, and T. Xiang, "Background Subtraction with DirichletProcess Mixture Models", IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 4, pp. 670-683, 2014.
[http://dx.doi.org/10.1109/TPAMI.2013.239] [PMID: 26353192]
[14]
P. Dollár, R. Appel, S. Belongie, and P. Perona, "Fast Feature Pyramids for Object Detection", IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 8, pp. 1532-1545, 2014.
[http://dx.doi.org/10.1109/TPAMI.2014.2300479] [PMID: 26353336]
[15]
P. Tokmakov, K. Alahari, and C. Schmid, "Learning Motion Patterns in Videos",. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 531-539
[16]
P.L. St-Charles, G.A. Bilodeau, and R. Bergevin, "SuBSENSE: A universal change detection method with local adaptive sensitivity", IEEE Trans. Image Process., vol. 24, no. 1, pp. 359-373, 2015.
[http://dx.doi.org/10.1109/TIP.2014.2378053] [PMID: 25494507]
[17]
C. Stauffer, and W. Grimson, "“Adaptive background mixture models for real-time tracking”, Int. Conf. on Computer Vision & Pattern Recognition, IEEE", Comput. Soc., vol. 2, pp. 252-256, 1999.
[18]
M. Haque, M. Murshed, and M. Paul, "On stable dynamic background generation technique using gaussian mixture models for robust object detection",. In 5th International Conference on Advanced Video and Signal Based Surveillance, 2008, pp. 41-48
[http://dx.doi.org/10.1109/AVSS.2008.12]
[19]
K.K. Ng, and E.J. Delp, "Background subtraction using a pixel-wise adaptive learning rate for object tracking initialization", Proceedings of SPIE Digital Library Visual Information Processing and Communication, vol. II, p. 78820I, 2011.
[http://dx.doi.org/10.1117/12.872610]
[20]
(a) X. Zhou, C. Yang, and W. Yu, Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank RepresentationIEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, 2014, no. 3, pp. 597-610, .;
(20b) S. Lee, and C. Lee, "Low Complexity Background Subtraction Based on Spatial Similarity", Eurasip Journal on Image and video processing, pp. 2-16, 2014.
[21]
C. R. Jung, "Efficient Background Subtraction and Shadow Removal for Monochromatic Video Sequences", IEEE Transactionson Multimedia, vol. 11, no. 3, pp. 90-117, 2009.
[http://dx.doi.org/10.1109/TMM.2009.2012924]
[22]
D. K. Yadav, and K. Singh, "A Combined Approach of Kullback-Leibler Divergence Method and Background Subtraction for Moving Object Detection in Thermal Video", Infrared Physics and Technology, vol. 76, pp. 21-31, 2016.
[23]
L. Sharma, D. K. Yadav, and A. Singh, "Fisher’s Linear Discriminant Ratio based Threshold for Moving Human Detection in Thermal Video", Infrared Physics and Technology, vol. 78, pp. 118-128, 2016.
[http://dx.doi.org/10.1016/j.infrared.2016.07.012]
[24]
L. Sharma, and D. K. Yadav, "Histogram based Adaptive Learning Rate for Background Modelling and Moving Object Detection in Video Surveillance", International Journal of Telemedicine and Clinical Practices, vol. 2, no. 1, pp. 74-92, 2017.
[25]
M. Rai, R. Sharma, S. C. Satapathy, D. K. Yadav, T. Maity, and R.K. Yadav, "An Improved Statistical Approach for Moving Object Detection in Thermal Video Frames", Deep Learning Techniques for Infrared Image/Video Understanding, Multimedia Tools and Application, vol. 81, pp. 9289-9311, 2022.
[http://dx.doi.org/10.1007/s11042-021-11548-x]
[26]
I. Kajo, N. Kamel, and Y. Ruichek, "Incremental Tensor-Based Completion Method for Detection of Stationary Foreground Objects", IEEE Trans. Circ. Syst. Video Tech., vol. 29, no. 5, pp. 1325-1338, 2019.
[http://dx.doi.org/10.1109/TCSVT.2018.2841825]
[27]
H. Park, S. Park, and Y. Joo, "Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN", IEEE Access, vol. 8, pp. 80010-80019, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2990618]
[28]
F. Amin, A. Mondal, and J. Mathew, "A Large Dataset With a New Framework for Abandoned Object Detection in Complex Scenarios", IEEE Multimedia, vol. 28, no. 3, pp. 75-87, 2021.
[http://dx.doi.org/10.1109/MMUL.2021.3083701]
[29]
D. Kim, H. Kim, Y. Mok, and J. Paik, "HLDNet: Abandoned Object Detection Using Hand Luggage Detection Network", IEEE Consum. Electron. Mag., pp. 1-1, 2021.
[30]
W. Liu, P. Liu, C. Xiao, and R. Hu, "General-purpose Abandoned Object Detection Method without Background Modeling", In International Conference on Imaging Systems and Techniques (IST), 2021, pp. 1-5
[http://dx.doi.org/10.1109/IST50367.2021.9651400]
[31]
A. Alam, I. Ullah, and Y.K. Lee, "Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues", IEEE Access, vol. 8, pp. 152377-152422, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3017135]
[32]
D.K. Yadav, K. Singh, and S. Kumari, "Challenging Issues of Video Surveillance System using Internet of Things in Cloud Environment",. Advances in Computing and Data Sciences. ICACDS 2016., 2016, pp. 471-481. Ghaziabad.
[33]
S. Mishra, D. K. Yadav, F. Tabassum, and D. Kumar, "Detection of Moving Vehicle in Foggy Environment through Google’s Firebase Platform", Turkish Online Journal of Qualitative Inquiry, vol. 12, no. 6, pp. 9892-9901, 2021.
[34]
S. Pouyanfar, Y. Yang, S-C. Chen, M-L. Shyu, and S.S. Iyengar, "Multimedia big data analytics: A survey", ACM Comput. Surv., vol. 51, p. 10, 2018.
[35]
"Video Analytics Market", Available From: https://github.com/apache/rocketmq
[36]
"YouTube Statistics", Available From: https://www.youtube.com/about/press/
[37]
"Netflix", Available From: https://www.netflix.com/
[38]
I.A.T. Hashem, I. Yaqoob, N.B. Anuar, S. Mokhtar, A. Gani, and S. Ullah Khan, "The rise of “big data” on cloud computing: Review and open research issues", Inf. Syst., vol. 47, pp. 98-115, 2015.
[http://dx.doi.org/10.1016/j.is.2014.07.006]
[39]
N. Elgendy, and A. Elragal, "Big data analytics: A literature review paper",. In Proc. Int. Conf. Data Mining, 2014, pp. 214-227
[http://dx.doi.org/10.1007/978-3-319-08976-8_16]
[40]
D.M. Jiménez-Bravo, Á. Lozano Murciego, A. Sales Mendes, H. Sánchez San Blás, and J. Bajo, "Multi-object tracking in traffic environments: A systematic literature review", Neurocomputing, vol. 494, pp. 43-55, 2022.
[http://dx.doi.org/10.1016/j.neucom.2022.04.087]
[41]
N.H. Abdulghafoor, and H.N. Abdullah, "A novel real-time multiple objects detection and tracking framework for different challenges", Alex. Eng. J., vol. 61, no. 12, pp. 9637-9647, 2022.
[http://dx.doi.org/10.1016/j.aej.2022.02.068]
[42]
"Microsoft’s Wallflower Dataset", Available From: https://www.microsoft.com/en-us/research/publication/wallflowerprinciples-and-practice-of-background-maintenance/
[43]
"Change Detection Dataset", Available From: www.changedetection.net
[44]
M. Kumar, S. Ray, and D.K. Yadav, "Moving Human Detection and Tracking from Thermal Video through Intelligent Surveillance System for Smart Applications", Multimedia Tools and Applications, pp. 1-16, 2022.
[http://dx.doi.org/10.1007/s11042-022-13515-6]
[45]
P. Mercorelli, "Denoising and Harmonic Detection Using Nonorthogonal Wavelet Packets in Industrial Applications", J. Syst. Sci. Complex., vol. 20, no. 3, pp. 325-343, 2007.
[http://dx.doi.org/10.1007/s11424-007-9028-z]
[46]
P. Mercorelli, "Biorthogonal wavelet trees in the classification of embedded signal classes for intelligent sensors using machine learning applications", Journal of the. Franklin Institute, vol. 344, no. 6, pp. 813-829, 2007.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy