Enhanced Automated Framework For Cattle Tracking And Classification

• Main Author: Williams, Bello Rotimi
• Format: Thesis
• Language: English
• Published: 2022
• Subjects: QA75.5-76.95 Electronic computers. Computer science
• Online Access: http://eprints.usm.my/59675/1/24%20Pages%20from%20BELLO%20ROTIMI-WILLIAMS%20-%20TESIS.pdf; http://eprints.usm.my/59675/
• Institution: Universiti Sains Malaysia

Employing computer vision-based methods in monitoring individual cows has become what researchers are striving for. Computer vision-based methods could be used to monitor each individual cows. The accuracy of the existing methods and frameworks is below expectation in handling these tasks. Moreover, they can still be improved to achieve better and more accurate results. The goal of this research is to provide a framework for better cattle tracking and classification systems. An enhanced object tracking algorithm (PFtmM) that integrates enhanced particle filter algorithm (PFtm) with mean-shift tracker (M) is proposed and deployed as first step to address the problems arise due to occurrence of occlusion and non-linear movement of cow objects in video. The integration of particle filter with mean-shift tracker considers the following techniques: (1) temporary memory for keeping tracks of occluded cow objects; (2) supplementing each algorithm’s weakness by the strength of the other for tracking non-linear movement. Strength of particle filter (PF) is its non-linearity property which it uses to track object’s non-linear movement but, with high computational time and search range as its weakness. Temporary memory (tm) strength is its ability to track full occlusion with reduced computational time and search range. Mean-shift strength is its sensitivity to object’s movement and colour distribution by using similarity function but, with inability to track object’s non-linear movement and full occlusion as its weakness.