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

  • G. Jemilda Jayaraj Annapackiam CSI College of Engineering, Nazareth-628617. Tamilnadu, India.
  • S. Baulkani Government College of Engineering, Tirunelveli-627007. Tamil Nadu, India.


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.


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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: <>. Date accessed: 13 july 2020. doi:


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