Astro Data Group partners with large facility management firm to revolutionize their facility operations through cutting-edge computer vision and edge computing. By combining strategically placed cameras with real-time analytics, our solution provided the client with actionable insights to monitor activity, manage security, and streamline operations efficiently. Our team’s deep collaboration with their team ensured the technology seamlessly aligned with their business goals, delivering precision tracking, reduced data transfer, and scalable monitoring.
The client is a prominent US-based facility management company that specializes in offering comprehensive maintenance and management solutions for sizable commercial properties. Their range of services encompasses security, maintenance, landscaping and waste management.
Our client relied heavily on manual inspections to monitor and track various aspects of facility management, such as security and maintenance. This manual approach was time-consuming and prone to errors. It had limitations on real-time monitoring capabilities.
Ensuring compliance with safety regulations and identifying potential safety hazards within the facilities was a constant challenge for the company. Manual inspection methods cannot identifying all safety risks, leading to potential liabilities and compromised safety standards.
Astro Data Group initiated the consultation process by conducting a comprehensive assessment of the client’s facility management operations. Through collaborative planning sessions, we discussed the requirements and objectives in detail. Our computer vision specialists ensured the proposed solution aligned with their vision and business objectives. The solutions included:
Astro Data Group developed a computer vision solution that met the specific needs of the client. The solution included a network of cameras that were strategically placed throughout the facility. The cameras were used to capture images and videos of the facility, which were then analyzed by computer vision algorithms. The algorithms were able to identify people, vehicles, and other objects in images and videos.
The computer vision solution also included edge computing capabilities. This allowed the YOLO algorithms to run on the cameras themselves, rather than on a central server. This made the solution more scalable and efficient, as it reduced the amount of data that had to be transferred between the cameras and the server.
The computer vision solution enabled real-time monitoring of the facility. This allowed the client to track people and vehicles entering or leaving the premises. The solution also enabled the client to track unauthorized parking and take proactive measures.
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