Motion Boundary Detection Improved by Bio-Inspired Approach
(*) Corresponding author
Motion detection in video sequences is a problem that different techniques and algorithms have faced. Several computer vision applications require an efficient detection stage. A reliable motion detection system should work under a wide range of a variety of scenes and various acquisition conditions. In this paper, a new approach to detect moving objects boundaries in an image sequence is presented. The proposed approach is based on the frame differing of modified Haynes technique combined with a biologically inspired spiking neural network for boundaries detection. First, the detection of temporal changes consists in extracting the moving objects between two consecutive frames in a dynamic scene without any estimate of the optical flow, nor any priory on the stage as the background image. The main goal is to get a fast and effective detection. In order to obtain a motion boundaries information, the extracted moving objects are then presented to a spiking neural network based on the Hodgkin-Huxley neuron model whose responses can form a complete closed curve coinciding with the motion object boundaries. Simulation results in some experiments are satisfactory. The performance of the motion boundary detection system is compared against competing boundary detection method using the YouTube Motion Boundaries dataset (YMB) dataset. The comparison results obtained confirm the robustness of the proposed approach. The extracted boundaries objects results can be used in further applications.
Copyright © 2019 Praise Worthy Prize - All rights reserved.
Bouwmans T, Maddalena L, Petrosino A, Scene background initialization: a taxonomy. Pattern Recognition Letters, Volume 96, 1 September 2017, Pages 3-11.
Erichson NB, Donovan C, Randomized lowrank dynamic mode decomposition for motion detection. Computer Vision and Image Understanding, Volume 146, May 2016, Pages 40-50.
Ye X, Yang J, Sun X, Li K, Hou C, Wang Y, Foreground–background separation from video clips via motion-assisted matrix restoration, IEEE transactions on circuits and systems for video technology, vol. 25, no. 11, November 2015.
V. R. Satpute, K. D. Kulat, A. G. Keskar, A Novel Approach Based on 2D-DWT and Variance Method for Human Detection and Tracking in Video Surveillance Applications, International Journal of Advance Research in Science and Engineering IJARSE Vol. No.3, Issue No.7 July 2014.
Abdul Aziz, N., Mustafah, Y., Azman, A., Shafie, A., Yusoff, M., Zainuddin, N., Rashidan, M., Real-Time Moving Objects Tracking for Distributed Smart Video Surveillances, (2016) International Review on Computers and Software (IRECOS), 11 (4), pp. 324-335.
Moein Shakeri, Hong Zhang; Moving Object Detection in Time-Lapse or Motion Trigger Image Sequences Using Low-rank and Invariant Sparse Decomposition. The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 5123-5131.
Meftah B, Lézoray O, Chaturvedi S, Khurshid A, Benyettou A, Image processing with spiking neuron networks. In: Yang XS (ed) Artificial intelligence, evolutionary computation and metaheuristics, SCI 427. Springer, New York, pp 525– 544,2013.
Hayat Yedjour, Boudjelal Meftah, Olivier Lézoray, Abdelkader Benyettou, Edge detection based on Hodgkin–Huxley neuron model simulation, Cognitive Processing, vol. 18, no. 3, pp. 315-323. 2017.
Haynes, S. M., & Tain, R.. Detection of moving edges. Computer Vision, Graphics, and Image Processing, 21(3), 345–367, 1983.
Lassoued, I., Zagrouba, E. Human actions recognition: an approach based on stable motion boundary fields. Multimed Tools Appl 77: 20715. 2018.
Yi-Hsuan Tsai, Ming-Hsuan Yang, Michael J. Black. Video Segmentation via Object Flow. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Cao X, Yang L, Guo X , Total variation regularized RPCA for irregularly moving object detection under dynamic background. IEEE transactions on cybernetics 46(4):1014–1027, 2016.
Javed S, Jung SK, Mahmood A, Bouwmans T , Motion-aware graph regularized RPCA for background modeling of complex scenes. In:, 2016 23rd International Conference on Pattern Recognition (ICPR), IEEE, pp 120–125,2016.
Ortego D, SanMiguel JC, Mart´ınez JM, Rejection based multipath reconstruction for background estimation in video sequences with stationary objects. Computer vision and image understanding 147:23–37; 2016.
X. Liu, G. Zhao, J. Yao, and C. Qi. Background subtraction based on low-rank and structured sparse decomposition IEEE Transactions on Image Processing, 24(8):2502–2514, 2015.
Aihua Zheng, Tian Zou, Yumiao Zhao, Bo Jiang, Jin Tang, Chenglong Li. Background subtraction with multi-scale structured low-rank and sparse factorization. Neurocomputing, Volume 328, Pages 113-121, ISSN 0925-2312. 2019.
Zheng, A., Xu, M., Luo, B. et al. CLASS: Collaborative Low-Rank and Sparse Separation for Moving Object Detection. Cogn Comput 9: 180. 2017.
P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid.: Learning to Detect Motion BoundariesCVPR - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2015, Boston, United States. IEEE, Proceedings IEEE Conference on Computer Vision & Pattern Recognition, pp.2578-2586.
Peng, L., Zhang, F. & Zhou, B. Dynamic background modeling using tensor representation and ant colony optimization. Sci. China Math. 60: 2287. 2017.
Zhe Xu, Biao Min, Ray C.C. Cheung. A robust background initialization algorithm with superpixel motion detection. Signal Processing: Image Communication, Volume 71, Pages 1-12, ISSN 0923-5965. 2019.
Zhou X, Yang C, Yu W. Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans Pattern Anal Mach Intell, 35: 597–610. 2013.
Lyu, C., Liu, Y., Jiang, X. et al. High-Speed Object Tracking with Its Application in Golf Playing. Int J of Soc Robotics 9: 449. 2017.
Jingchun Cheng, Yi-Hsuan Tsai, Shengjin Wang and Ming-Hsuan Yang. SegFlow: Joint Learning for Video Object Segmentation and Optical Flow. IEEE International Conference on Computer Vision (ICCV), 2017.
Kishi, R.M., Trojahn, T.H. & Goularte, R. Correlation based feature fusion for the temporal video scene segmentation task. Multimed Tools Appl (2019) 78: 15623. 2019.
Basuki, R., Hariadi, M., Yuniarno, E., Purnomo, M., Spectral-Based Temporal-Constraint Estimation for Semi-Automatic Video Object Segmentation, (2015) International Review on Computers and Software (IRECOS), 10 (9), pp. 959-965.
G. Shobha, N. Satish Kumar, Adaptive Background Modeling and Foreground Detection in Video Sequence Using Artificial Neural Network, International Conference on Intelligent Computational Systems, ICICS 2012, Dubai, January.
Yong Wang, Xinbin Luo, Lu Ding, Shan Fu, Xian Wei. Detection based visual tracking with convolutional neural network, Knowledge-Based Systems, Volume 175, Pages 62-71, ISSN 0950-7051. 2019.
B. Chen, S. Huang, J. Yen, Counter-propagation artificial neural network-based motion detection algorithm for static-camera surveillance scenarios, Neurocomputing, Volume 273, pages 481–493, 2018.
G. Orchard, R. Benosman, R. Etienne-Cummings, and N.V. Thakor. A spiking neural network architecture for visual motion estimation. In IEEE Biomedical Circuits and Systems Conf. (BioCAS), pages 298– 301. IEEE, 2013.
Bin Hu, Zhuhong Zhang, Bio-plausible visual neural network for spatio-temporally spiral motion perception, Neurocomputing, Volume 310, Pages 96-114, ISSN 0925-2312. 2018.
Risinger, L. & Kaikhah, K. Motion detection and object tracking with discrete leaky integrate-and-fire neurons. Appl Intell (2008) 29: 248. 2008.
Joukes J, Hartmann TS and Krekelberg B (2014) Motion detection based on recurrent network dynamics. Front. Syst. Neurosci. 8-239, 2014.
Mohammadreza Babaee, Duc Tung Dinh, Gerhard Rigoll. A deep convolutional neural network for video sequence background subtraction. Pattern Recognition, Volume 76, Pages 635-649, ISSN 0031-3203. 2018.
Jimenez-Moreno, R., Martinez, D., A Novel Parallel Convolutional Network Architecture for Depth-Dependent Object Recognition, (2019) International Review of Automatic Control (IREACO), 12 (2), pp. 76-81.
Shuchao Pang, Juan José del Coz, Zhezhou Yu, Oscar Luaces, Jorge Díez. Deep learning to frame objects for visual target tracking, Engineering Applications of Artificial Intelligence, Volume 65, Pages 406-420, ISSN 0952-1976. 2017.
Azzopardi G, Petkov N, A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biol Cybern 106(3):177–189, 2012.
Useche-Murillo, P., Jimenez-Moreno, R., Pinzon-Arenas, J., Classification of Objects with Occlusions by Means of a DAG-CNN, (2018) International Review of Automatic Control (IREACO), 11 (6), pp. 346-353.
Pinzon Arenas, J., Jimenez Moreno, R., Hernandez Beleño, R., EMG Signal Acquisition and Processing Application with CNN Testing for MATLAB, (2018) International Review of Automatic Control (IREACO), 11 (1), pp. 44-51.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2023 Praise Worthy Prize