Abstract: The existence of illumination variation, non-rigid object, occlusion, non-linear motion, and real-time implementation requirement has made tracking in computer vision a challenging task. In order to recognize individual cow and to mitigate all the challenging tasks, an image processing system is proposed using the body pattern images of the cow. This system accepts an input image, performs processing operation on the image, and output results in form of classification under certain categories. Technically, convolutional neural network is modeled for the training and testing of each pattern image of 1000 acquired images of 10 species of cow which will pass it through a series of convolution layers with filters, pooling, fully connected layers and softmax function for the pattern images classification with probabilistic values between 0 and 1. The performance evaluation of the proposed system for both training and testing data was carried out for each cow’s identification and 92.59% and 89.95% accuracies were achieved respectively.

Rotimi-Williams Bello, Abdullah Zawawi Talib, Ahmad Sufril Azlan Mohamed, Daniel A. Olubummo and Firstman Noah Otobo, “Image-based Individual Cow Recognition using Body Patterns” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110311

@article{Bello2020,
title = {Image-based Individual Cow Recognition using Body Patterns},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110311},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110311},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Rotimi-Williams Bello and Abdullah Zawawi Talib and Ahmad Sufril Azlan Mohamed and Daniel A. Olubummo and Firstman Noah Otobo}
}