The latest report by Reports and Data reveals that the global Artificial Intelligence (AI) Based Fever Detection Camera Market was USD 2.38 Billion in 2022. It is projected to experience a revenue compound annual growth rate (CAGR) of 9.0% during the forecast period. The increasing demand for non-contact temperature screening, especially among patients, is a significant driver for the market’s revenue growth. These cameras offer efficient and rapid temperature screening, reducing the risk of infection transmission. Furthermore, the market is witnessing a rise in demand due to the development of cutting-edge technologies like AI, machine learning, and cloud-based solutions. The adoption of these advanced techniques enables real-time monitoring of patients. Consequently, these technological advancements, combined with government initiatives promoting effective screening techniques, are expected to fuel market growth.
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Key Highlights from the Report:
- The market is categorized into turret/bullet cameras and handheld cameras. Among these, turret/bullet cameras are anticipated to dominate the market in terms of revenue share. Their compact size, easy installation, affordability, and compatibility with other systems like face recognition and access control contribute to their growing adoption, driving the segment’s growth.
- According to regional analysis, North America is expected to hold the largest revenue share in 2022. This can be attributed to the increasing adoption of technological advancements, particularly in healthcare, hospitals, and transportation sectors, resulting in higher demand for non-contact temperature screening. Additionally, the strong presence of key players, leading to product launches, acquisitions, and mergers, further contributes to the region’s market growth.
- In 2022, FLIR Systems introduced the FLIR A65, an AI-based fever detection camera utilizing thermal imaging technology and machine learning algorithms for accurate real-time temperature detection.
Driving factors of Artificial Intelligence (AI) Based Fever Detection Camera Market
- Rising Demand for Non-Contact Temperature Screening: There is a growing demand for non-contact temperature screening among the patient population. AI-based fever detection cameras provide effective and quick temperature screening, reducing the risk of infection transmission.
- Advancements in Technology: The development of cutting-edge technologies like AI, machine learning, and cloud-based solutions has fueled the demand for AI-based fever detection cameras. These technological advancements enable real-time monitoring and enhance the accuracy and efficiency of temperature screening.
- Government Initiatives: Governments are focusing on initiatives to promote the use of effective and efficient screening techniques. This support from governments has further boosted the adoption of AI-based fever detection cameras, driving the market growth.
- Integration with Other Systems: Fever detection cameras, particularly turret/bullet cameras, can easily integrate with other systems such as face recognition systems and access control systems. This compatibility and versatility contribute to their increasing adoption, leading to market growth.
- Healthcare Industry Demand: The healthcare industry has witnessed a significant demand for AI-based fever detection cameras. Hospitals, clinics, and healthcare facilities are adopting these cameras to enhance their screening processes and ensure the safety of patients and staff.
- Technological Advancements in North America: North America, specifically the United States, has been at the forefront of technological advancements in the AI-based fever detection camera market. The region’s strong presence of key players, frequent product launches, acquisitions, and mergers have contributed to the market’s growth.
Some of the notable innovations in the Artificial Intelligence (AI) Based Fever Detection Camera Market
- Thermal Imaging Technology: AI-based fever detection cameras utilize advanced thermal imaging technology to detect elevated body temperatures accurately. This technology allows for quick and non-invasive temperature screening, making it a valuable tool in various industries.
- Machine Learning Algorithms: The integration of machine learning algorithms enhances the accuracy and efficiency of fever detection cameras. These algorithms enable the cameras to learn and adapt to different scenarios, improving their ability to detect anomalies and identify individuals with elevated temperatures.
- Real-time Monitoring: AI-based fever detection cameras offer real-time monitoring capabilities, allowing for immediate alerts and notifications when individuals with high temperatures are detected. This feature enables prompt response and appropriate measures to be taken in a timely manner.
- Cloud-based Solutions: The adoption of cloud-based solutions in fever detection cameras enables seamless data storage, analysis, and remote access. Cloud connectivity enhances the scalability and flexibility of the cameras, allowing for centralized monitoring and management.
- Integration with Other Systems: Fever detection cameras can be integrated with other systems, such as access control systems, facial recognition systems, and centralized security systems. This integration enhances overall security and access management while maintaining efficient temperature screening.
- Artificial Intelligence-driven Analytics: AI algorithms analyze the captured data from fever detection cameras, providing valuable insights and trends. These analytics can help in identifying potential hotspots, monitoring crowd density, and facilitating effective decision-making for better infection control measures.
Major players in the market include
FLIR Systems, Inc., Dahua Technology Co., Ltd., Axis Communications AB, ZKTeco Co., Ltd., Hikvision Digital Technology Co., Ltd., A4Vision, Inc., Bosch Security Systems Inc., Thermoteknix Systems Ltd., Optex Co., Ltd., Keyence Corporation, Panasonic Corporation, NEC Corporation, Sony Corporation, Fujifilm Corporation, and Toshiba Corporation.
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