Abstract—A commonly used approach for detecting objects is based on the techniques of “boosting” and “cascading”, which allow for real-time detection. In this paper I have developed a classifier for detecting horses from images or from real time video sources. For that purpose the Haar-like features were used to discriminate horses. Those features were used as input in a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields an extremely efficient classifier.
Index Terms—Horse detection, object detection, haar-like features, adaboost.
Mohammad Salah Uddin is with Dipartimento di Ingegneria Informatica Automatica e Gestionale Antonio Ruberti, Sapienza Universit`a di Roma, Rome, Italy (e-mail: uddin@dis.uniroma1.it).
Afroza Yesmin Akhi is with the Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh (e-mail: ankhi.ayaz@yahoo.com).
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Cite:M. S. Uddin and A. Y. Akhi, "Horse Detection Using Haar Like Features," International Journal of Computer Theory and Engineering vol. 8, no. 5, pp. 415-418, 2016.
