Occupancy monitoring is an important research problem with a broad range of applications in security, surveillance, and resource management in smart building environments. As a result, it has immediate solutions to solving some of society's most pressing issues. For example, HVAC and lighting systems in the US consume approximately 45-50% of the total energy a building uses. Smart buildings can reduce wasted energy by incorporating networkable occupancy sensors to obtain real-time occupancy data for the facilities. Therefore, occupancy monitoring systems can enable significant cost savings and carbon reduction. In addition, workplaces have quickly adapted and implemented COVID-19 safety measures by preventing overcrowding using real-time information on people density. While there are many sensors, RGB cameras have proven to be the most accurate. However, cameras create privacy concerns. Hence, our research aims to design an efficient occupancy monitoring system with minimal privacy invasion. We conducted a systematic study on sensor characterization using various low-resolution infrared sensors and proposed a unified processing algorithms pipeline for occupancy estimation. This research also investigates low-resolution thermal imaging sensors with a chessboard reading pattern, focusing on algorithm design issues and proposing solutions when detecting moving objects. Our proposed approach achieves about 99% accuracy in occupancy estimation, enabling many practical smart building applications. We also added additional sensors to our system using sensor fusion technology to boost its functionality and demonstrated the system's feasibility by deploying it in different real-time application scenarios.