Counter-propagation artificial neural network-based motion detection algorithm for static-camera surveillance scenarios
Motion detection plays an important role in most static-camera video surveillance systems, yet video communications over wireless networks can easily suffer from network congestion or unstable bandwidth, especially for embedded applications. A rate control scheme produces variable bit rate video str...
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| Published in | Neurocomputing (Amsterdam) Vol. 273; pp. 481 - 493 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
17.01.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 1872-8286 |
| DOI | 10.1016/j.neucom.2017.08.002 |
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| Abstract | Motion detection plays an important role in most static-camera video surveillance systems, yet video communications over wireless networks can easily suffer from network congestion or unstable bandwidth, especially for embedded applications. A rate control scheme produces variable bit rate video streams to match the available network bandwidth. However, effectively detecting moving objects in a variable bit rate video stream is a considerable challenge. This paper proposes an advanced approach based on a counter-propagation artificial neural network to achieve effective moving-object detection in such conditions. Qualitative and quantitative tests over real-world limited bandwidth networks show that the proposed method substantially outperforms other state-of-the-art methods. |
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| AbstractList | Motion detection plays an important role in most static-camera video surveillance systems, yet video communications over wireless networks can easily suffer from network congestion or unstable bandwidth, especially for embedded applications. A rate control scheme produces variable bit rate video streams to match the available network bandwidth. However, effectively detecting moving objects in a variable bit rate video stream is a considerable challenge. This paper proposes an advanced approach based on a counter-propagation artificial neural network to achieve effective moving-object detection in such conditions. Qualitative and quantitative tests over real-world limited bandwidth networks show that the proposed method substantially outperforms other state-of-the-art methods. |
| Author | Huang, Shih-Chia Chen, Bo-Hao Yen, Jui-Yu |
| Author_xml | – sequence: 1 givenname: Bo-Hao surname: Chen fullname: Chen, Bo-Hao email: bhchen@saturn.yzu.edu.tw organization: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan – sequence: 2 givenname: Shih-Chia surname: Huang fullname: Huang, Shih-Chia email: schuang@ntut.edu.tw organization: Department of Electronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan – sequence: 3 givenname: Jui-Yu surname: Yen fullname: Yen, Jui-Yu email: tim904021@hotmail.com organization: Department of Electronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan |
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| References | Cheng, Huang, Ruan (bib0007) 2011; 41 Manzanera, Richefeu (bib0012) 2007; 28 Jodoin, Mignotte, Konrad (bib0014) 2007; 17 Wiegand, Sullivan, Bjntegaard, Luthra (bib0027) 2003; 13 Huang, Do (bib0004) 2014; 44 St-Charles, Bilodeau, Bergevin (bib0017) 2014 Huang, Chen (bib0019) 2013; 24 Taati, Snoek, Mihailidis (bib0002) 2013; 100 Ko, Soatto, Estrin (bib0010) 2008 Chen, Huang (bib0024) 2012 St-Charles, Bilodeau (bib0018) 2014 Yen, Chen, Huang (bib0025) 2012 Cyganek, Gruszczynski (bib0001) 2014; 126 Chen, Huang (bib0021) 2015; 299 Buntine, Weigend (bib0026) 1994; 5 Do, Huang (bib0008) 2011 Ha, Lee (bib0015) 2010; 49 Wang, Zheng, Shi, Xue, Liu, He (bib0005) 2015; 151 Chen, Huang (bib0023) 2013 Manzanera, Richefeu (bib0011) 2004 Huang, Chen (bib0020) 2014; 61 Zhou, Zhang (bib0013) 2005; 3 Chen, Shi, Ke (bib0028) 2017 Lillo-Castellano, Mora-Jimnez, Figuera-Pozuelo, Rojo-Ivarez (bib0003) 2015; 153 Guraya, Cheikh (bib0009) 2015; 149 Barnich, Droogenbroeck (bib0016) 2011; 20 Chen, Huang (bib0022) 2014; 16 Chen, Chang, Huang (bib0006) 2016 Chen (10.1016/j.neucom.2017.08.002_bib0023) 2013 Jodoin (10.1016/j.neucom.2017.08.002_bib0014) 2007; 17 Manzanera (10.1016/j.neucom.2017.08.002_bib0011) 2004 Cyganek (10.1016/j.neucom.2017.08.002_bib0001) 2014; 126 Chen (10.1016/j.neucom.2017.08.002_bib0028) 2017 Huang (10.1016/j.neucom.2017.08.002_bib0004) 2014; 44 Zhou (10.1016/j.neucom.2017.08.002_bib0013) 2005; 3 Manzanera (10.1016/j.neucom.2017.08.002_bib0012) 2007; 28 Wiegand (10.1016/j.neucom.2017.08.002_bib0027) 2003; 13 Chen (10.1016/j.neucom.2017.08.002_bib0006) 2016 Lillo-Castellano (10.1016/j.neucom.2017.08.002_bib0003) 2015; 153 Cheng (10.1016/j.neucom.2017.08.002_bib0007) 2011; 41 Ha (10.1016/j.neucom.2017.08.002_bib0015) 2010; 49 Chen (10.1016/j.neucom.2017.08.002_bib0021) 2015; 299 Yen (10.1016/j.neucom.2017.08.002_bib0025) 2012 Buntine (10.1016/j.neucom.2017.08.002_bib0026) 1994; 5 Barnich (10.1016/j.neucom.2017.08.002_bib0016) 2011; 20 St-Charles (10.1016/j.neucom.2017.08.002_bib0018) 2014 Guraya (10.1016/j.neucom.2017.08.002_sbref0009) 2015; 149 Chen (10.1016/j.neucom.2017.08.002_bib0022) 2014; 16 Chen (10.1016/j.neucom.2017.08.002_bib0024) 2012 Huang (10.1016/j.neucom.2017.08.002_bib0019) 2013; 24 Taati (10.1016/j.neucom.2017.08.002_bib0002) 2013; 100 Do (10.1016/j.neucom.2017.08.002_bib0008) 2011 Huang (10.1016/j.neucom.2017.08.002_bib0020) 2014; 61 Ko (10.1016/j.neucom.2017.08.002_bib0010) 2008 St-Charles (10.1016/j.neucom.2017.08.002_bib0017) 2014 Wang (10.1016/j.neucom.2017.08.002_sbref0005) 2015; 151 |
| References_xml | – volume: 149 start-page: 1348 year: 2015 end-page: 1359 ident: bib0009 article-title: Neural networks based visual attention model for surveillance videos publication-title: Neurocomputing – volume: 5 start-page: 480 year: 1994 end-page: 488 ident: bib0026 article-title: Computing second derivatives in feed-forward networks: a review publication-title: IEEE Trans. Neural Netw. – start-page: 414 year: 2014 end-page: 419 ident: bib0017 article-title: Flexible background subtraction with self-balanced local sensitivity publication-title: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops – volume: 299 start-page: 283 year: 2015 end-page: 295 ident: bib0021 article-title: Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems publication-title: Inf. Sci. – volume: 49 start-page: 047 year: 2010 end-page: 201 ident: bib0015 article-title: Foreground objects detection using multiple difference images publication-title: Opt. Eng. – volume: 20 start-page: 1709 year: 2011 end-page: 1724 ident: bib0016 article-title: Vibe: A universal background subtraction algorithm for video sequences publication-title: IEEE Trans. Image Process. – volume: 17 start-page: 1758 year: 2007 end-page: 1763 ident: bib0014 article-title: Statistical background subtraction using spatial cues publication-title: IEEE Trans. Circuits Syst. Video Technol. – start-page: 717 year: 2012 end-page: 720 ident: bib0025 article-title: Enhanced extraction of moving objects in variable bit-rate video streams publication-title: Proceedings of ACM international conference on Multimedia – volume: 24 start-page: 1920 year: 2013 end-page: 1931 ident: bib0019 article-title: Highly accurate moving object detection in variable-bit-rate video-based traffic monitoring systems publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 28 start-page: 320 year: 2007 end-page: 328 ident: bib0012 article-title: A new motion detection algorithm based on publication-title: Pattern Recognit. Lett. – volume: 100 start-page: 163 year: 2013 end-page: 169 ident: bib0002 article-title: Video analysis for identifying human operation difficulties and faucet usability assessment publication-title: Neurocomputing – start-page: 134 year: 2017 end-page: 141 ident: bib0028 article-title: Low-rank representation with contextual regularization for moving object detection in big surveillance video data publication-title: Proceedings of IEEE Third International Conference on Multimedia Big Data, Laguna Hills, CA – volume: 61 start-page: 2099 year: 2014 end-page: 2112 ident: bib0020 article-title: Automatic moving object extraction through a real world variable-bandwidth network for traffic monitoring systems publication-title: IEEE Trans. Ind. Electron. – volume: 126 start-page: 78 year: 2014 end-page: 94 ident: bib0001 article-title: Hybrid computer vision system for drivers’ eye recognition and fatigue monitoring publication-title: Neurocomputing – volume: 3 start-page: 2224 year: 2005 end-page: 2229 ident: bib0013 article-title: Modified GMM background modeling and optical flow for detection of moving objects publication-title: Proceedings of International Conference on Systems, Man, and Cybernetics – start-page: 276 year: 2008 end-page: 289 ident: bib0010 article-title: Background subtraction with distributions publication-title: Proceedings of the European Conference on Computer Vision – start-page: 46 year: 2004 end-page: 51 ident: bib0011 article-title: A robust and computationally efficient motion detection algorithm based on publication-title: Proceedings of Indian Conference on Vision, Graphics and Image Processing, ICVGIP’04 – volume: 41 start-page: 589 year: 2011 end-page: 598 ident: bib0007 article-title: Scene analysis for object detection in advanced surveillance systems using laplacian distribution model publication-title: IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) – start-page: 2505 year: 2012 end-page: 2509 ident: bib0024 article-title: A novel moving vehicles extraction algorithm over wireless internet publication-title: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics – start-page: 1 year: 2011 end-page: 4 ident: bib0008 article-title: Dynamic background modeling based on radial basis function neural networks for moving object detection publication-title: Proceedings of IEEE International Conference on Multimedia and Expo, Barcelona – start-page: 69 year: 2013 end-page: 75 ident: bib0023 article-title: Accurate detection of moving objects in traffic video streams over limited bandwidth networks publication-title: Proceedings of IEEE International Symposium on Multimedia – start-page: 509 year: 2014 end-page: 515 ident: bib0018 article-title: Improving background subtraction using local binary similarity patterns publication-title: Proceedings of IEEE Winter Conference on Applications of Computer Vision – volume: 151 start-page: 1500 year: 2015 end-page: 1506 ident: bib0005 article-title: Embedding metric learning into set-based face recognition for video surveillance publication-title: Neurocomputing – volume: 13 start-page: 560 year: 2003 end-page: 576 ident: bib0027 article-title: Overview of the h.264/AVC video coding standard publication-title: IEEE Trans. Circuits Syst. Video Technol. – start-page: 439 year: 2016 end-page: 444 ident: bib0006 article-title: Denoising using inverse-distance weighting with sparse approximation publication-title: Proceedings of IEEE International Symposium on Multimedia (ISM), San Jose, CA – volume: 44 start-page: 114 year: 2014 end-page: 125 ident: bib0004 article-title: Radial basis function based neural network for motion detection in dynamic scenes publication-title: IEEE Trans. Cyber. – volume: 153 start-page: 286 year: 2015 end-page: 299 ident: bib0003 article-title: Traffic sign segmentation and classification using statistical learning methods publication-title: Neurocomputing – volume: 16 start-page: 837 year: 2014 end-page: 847 ident: bib0022 article-title: An advanced moving object detection algorithm for automatic traffic monitoring in real-world limited bandwidth networks publication-title: IEEE Trans. Multimed. – volume: 100 start-page: 163 year: 2013 ident: 10.1016/j.neucom.2017.08.002_bib0002 article-title: Video analysis for identifying human operation difficulties and faucet usability assessment publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.10.041 – volume: 151 start-page: 1500 year: 2015 ident: 10.1016/j.neucom.2017.08.002_sbref0005 article-title: Embedding metric learning into set-based face recognition for video surveillance publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.10.032 – volume: 44 start-page: 114 issue: 1 year: 2014 ident: 10.1016/j.neucom.2017.08.002_bib0004 article-title: Radial basis function based neural network for motion detection in dynamic scenes publication-title: IEEE Trans. Cyber. doi: 10.1109/TCYB.2013.2248057 – volume: 61 start-page: 2099 issue: 4 year: 2014 ident: 10.1016/j.neucom.2017.08.002_bib0020 article-title: Automatic moving object extraction through a real world variable-bandwidth network for traffic monitoring systems publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2013.2262764 – start-page: 717 year: 2012 ident: 10.1016/j.neucom.2017.08.002_bib0025 article-title: Enhanced extraction of moving objects in variable bit-rate video streams – volume: 5 start-page: 480 issue: 3 year: 1994 ident: 10.1016/j.neucom.2017.08.002_bib0026 article-title: Computing second derivatives in feed-forward networks: a review publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.286919 – start-page: 509 year: 2014 ident: 10.1016/j.neucom.2017.08.002_bib0018 article-title: Improving background subtraction using local binary similarity patterns – volume: 24 start-page: 1920 issue: 12 year: 2013 ident: 10.1016/j.neucom.2017.08.002_bib0019 article-title: Highly accurate moving object detection in variable-bit-rate video-based traffic monitoring systems publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2013.2270314 – start-page: 2505 year: 2012 ident: 10.1016/j.neucom.2017.08.002_bib0024 article-title: A novel moving vehicles extraction algorithm over wireless internet – volume: 149 start-page: 1348 year: 2015 ident: 10.1016/j.neucom.2017.08.002_sbref0009 article-title: Neural networks based visual attention model for surveillance videos publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.08.062 – volume: 20 start-page: 1709 issue: 6 year: 2011 ident: 10.1016/j.neucom.2017.08.002_bib0016 article-title: Vibe: A universal background subtraction algorithm for video sequences publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2010.2101613 – start-page: 276 year: 2008 ident: 10.1016/j.neucom.2017.08.002_bib0010 article-title: Background subtraction with distributions – volume: 17 start-page: 1758 issue: 12 year: 2007 ident: 10.1016/j.neucom.2017.08.002_bib0014 article-title: Statistical background subtraction using spatial cues publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2007.906935 – volume: 153 start-page: 286 year: 2015 ident: 10.1016/j.neucom.2017.08.002_bib0003 article-title: Traffic sign segmentation and classification using statistical learning methods publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.11.026 – start-page: 439 year: 2016 ident: 10.1016/j.neucom.2017.08.002_bib0006 article-title: Denoising using inverse-distance weighting with sparse approximation – volume: 3 start-page: 2224 year: 2005 ident: 10.1016/j.neucom.2017.08.002_bib0013 article-title: Modified GMM background modeling and optical flow for detection of moving objects – volume: 126 start-page: 78 year: 2014 ident: 10.1016/j.neucom.2017.08.002_bib0001 article-title: Hybrid computer vision system for drivers’ eye recognition and fatigue monitoring publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.01.048 – volume: 16 start-page: 837 issue: 3 year: 2014 ident: 10.1016/j.neucom.2017.08.002_bib0022 article-title: An advanced moving object detection algorithm for automatic traffic monitoring in real-world limited bandwidth networks publication-title: IEEE Trans. Multimed. doi: 10.1109/TMM.2014.2298377 – volume: 13 start-page: 560 issue: 7 year: 2003 ident: 10.1016/j.neucom.2017.08.002_bib0027 article-title: Overview of the h.264/AVC video coding standard publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2003.815165 – volume: 41 start-page: 589 issue: 5 year: 2011 ident: 10.1016/j.neucom.2017.08.002_bib0007 article-title: Scene analysis for object detection in advanced surveillance systems using laplacian distribution model publication-title: IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) doi: 10.1109/TSMCC.2010.2092425 – start-page: 46 year: 2004 ident: 10.1016/j.neucom.2017.08.002_bib0011 article-title: A robust and computationally efficient motion detection algorithm based on Σ−Δ background estimation – start-page: 69 year: 2013 ident: 10.1016/j.neucom.2017.08.002_bib0023 article-title: Accurate detection of moving objects in traffic video streams over limited bandwidth networks – volume: 28 start-page: 320 year: 2007 ident: 10.1016/j.neucom.2017.08.002_bib0012 article-title: A new motion detection algorithm based on Σ−Δ background estimation publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2006.04.007 – start-page: 1 year: 2011 ident: 10.1016/j.neucom.2017.08.002_bib0008 article-title: Dynamic background modeling based on radial basis function neural networks for moving object detection – start-page: 414 year: 2014 ident: 10.1016/j.neucom.2017.08.002_bib0017 article-title: Flexible background subtraction with self-balanced local sensitivity – volume: 49 start-page: 047 issue: 4 year: 2010 ident: 10.1016/j.neucom.2017.08.002_bib0015 article-title: Foreground objects detection using multiple difference images publication-title: Opt. Eng. – start-page: 134 year: 2017 ident: 10.1016/j.neucom.2017.08.002_bib0028 article-title: Low-rank representation with contextual regularization for moving object detection in big surveillance video data – volume: 299 start-page: 283 year: 2015 ident: 10.1016/j.neucom.2017.08.002_bib0021 article-title: Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.12.033 |
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