Moving Object Detection and Tracking using Genetic Algorithm Enabled Extreme Learning Machine

  • G. Jemilda Jayaraj Annapackiam CSI College of Engineering, Nazareth-628617. Tamilnadu, India. http://orcid.org/0000-0002-8647-3567
  • S. Baulkani Government College of Engineering, Tirunelveli-627007. Tamil Nadu, India.

Abstract

In this proposed work, the moving object is localized using curvelet transform, soft thresholding and frame differencing. The feature extraction techniques are applied on to the localized object and the texture, color and shape information of objects are considered. To extract the shape information, Speeded Up Robust Features (SURF) is used. To extract the texture features, the Enhanced Local Vector Pattern (ELVP) and to extract color features, Histogram of Gradient (HOG) are used and then reduced feature set obtained using genetic algorithm are fused to form a single feature vector and given into the Extreme Learning Machine (ELM) to classify the objects. The performance of the proposed work is compared with Naive Bayes, Support Vector Machine, Feed Forward Neural Network and Probabilistic Neural Network and inferred that the proposed method performs better.

Author Biographies

G. Jemilda, Jayaraj Annapackiam CSI College of Engineering, Nazareth-628617. Tamilnadu, India.
 Professor, Department of Computer Science and Engineering,
S. Baulkani, Government College of Engineering, Tirunelveli-627007. Tamil Nadu, India.
ECE.

References

[1] Biswas, M.; Om H. (2012); A new soft thresholding Image Denoising method, Science Direct, 6:10-15,2012.
https://doi.org/10.1016/j.protcy.2012.10.002

[2] Cheng, H.-Y.; Weng, C.-C.; Chen Y.-Y.(2012); Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks, IEEE Transactions on Image Processing, 21(4): 2152- 2159, 2012.
https://doi.org/10.1109/TIP.2011.2172798

[3] Fadel, E.; Faheem, M.; Gungor, V.; Nassef, L.; Akkari, N.; Malik, M. (2017); Spectrum- Aware Bio-Inspired Routing in Cognitive Radio Sensor Networks for Smart Grid Applications Computer Communications, 106-120, 2017.

[4] Faheem, M.; Tuna, G.; Gungor, V.C. (2016); LRP: Link quality- aware queue- based spectral clustering routing protocol for underwater acoustic sensor networks, International Journal of Communication Systems, 2016.

[5] Faheem, M.; Gungor V.C. (2017); Energy Efficient and QoS-aware Routing Protocol for Wireless Sensor Network-based Smart Grid Applications in the Context of Industry 4.0, Applied Soft Computing, 1-13, 2017.
https://doi.org/10.1016/j.asoc.2017.07.045

[6] Faheem, M.; Tuna, G.; Gungor V.C. (2017); QERP: Quality-of-Service (QoS) Aware Evolutionary Routing Protocol for Underwater Wireless Sensor Networks, IEEE Systems Journal, 2017.

[7] Fan, K.-K.; Hung, T.-Y. (2014); A Novel Local Pattern Descriptor-Local Vector Pattern in High-Order Derivative Space for Face Recognition, IEEE Transactions on Image Processing, 23(7), 2877 - 2891, 2014.
https://doi.org/10.1109/TIP.2014.2321495

[8] Kimori, Y. (2013); Morphological image processing for quantitative shape analysis of biomedical structures: effective contrast enhancement, Journal of Synchrotron Radiation, 1(20), 848-853, 2013.
https://doi.org/10.1107/S0909049513020761

[9] Kourav, A.; Singh P. (2013); Review on curvelet transform and its applications, Asian Journal of Electrical Sciences, 2(1): 9-13, 2013.

[10] Li, Y.; Su G. (2015); Simplified histograms of oriented gradient features extraction algorithm for the hardware implementation, International Conference on Computers, Communications and Systems (ICCCS), 192 -195, 2015.
https://doi.org/10.1109/CCOMS.2015.7562899

[11] Philip, F.M.; Mukesh R.(2016); Hybrid tracking model for multiple object videos using second derivative based visibility model and tangential weighted spatial tracking model, International Journal of Computational Intelligence Systems, 9(5): 888-899, 2016.
https://doi.org/10.1080/18756891.2016.1237188

[12] Roy, A.; Shinde,S.; Kang, K.-D. (2012); An Approach for Efficient Real Time Moving Object Detection, International Journal of Signal Processing, Image Processing and Pattern Recognition, 5(3), 2012.

[13] Shingade, A.; Ghotkar A.(2014); Survey of Object Tracking and Feature Extraction Using Genetic Algorithm, International Journal of Computer Science and Technology, 5(1), 2014.

[14] Wang, Y.; Cao, F.; Yuan, Y. (2014); A Study on Effectiveness of Extreme Learning Machine, arXiv:1409.3924v1 [cs.NE], 13, 2014.

[15] Zohrevand, A.; Ahmadyfard, A.; Pouyan, A.; Imani, Z. (2014); A SIFT based object recognition using contextual information, Iranian Conference on Intelligent Systems (ICIS), 1-4, 2014.
Published
2018-04-13
How to Cite
JEMILDA, G.; BAULKANI, S.. Moving Object Detection and Tracking using Genetic Algorithm Enabled Extreme Learning Machine. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 2, p. 162-174, apr. 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3064>. Date accessed: 13 july 2020. doi: https://doi.org/10.15837/ijccc.2018.2.3064.

Keywords

curvelet transform, speeded up robust features, enhanced local vector pattern, histogram of gradient, extreme learning machine, genetic algorithm