A Smart Surveillance and Clocking System for Real-Time Monitoring of the Farm Environment

The commercial farming sector is plagued by farm attacks, theft of livestock and employee clocking fraud. The majority of surveillance systems currently in use are ineffective in detecting time fraud and unauthorized intrusions. These problems lead to the loss of significant assets and jeopardise the safety of the farm employers and employees. Using advanced gait recognition and unsupervised machine learning (ML) technologies, this study developed a highly accurate, smart clocking and surveillance system to monitor employees and send real-time notifications when an intrusion is detected.