## 1 Introduction  
### Research Background and Purpose  
Climate change, aging infrastructure, new types of mobility, and unspecified criminal accidents have increased the frequency of abnormal urban situations that are difficult to observe and predict. In this sense, demand has increased for real-time monitoring, responses, and prevention of urban life safety anomaly that threatens people's lives. CCTV is a powerful means for monitoring the life safety anomalies experienced by people. For instance, the New York City Police Department in the United States operates real-time responses and surveillance systems via the Domain Awareness System (DAS). This system is an integrated system for real-time crime monitoring developed by Microsoft and the Department, by linking CCTV information with police database information [1]. China is operating an active urban surveillance system via CCTV-based facial recognition technology, such as Sense Time or Megvii [2]. South Korea is operating CCTV control centers in 226 (lower level) local governments to monitor urban anomalies (e.g., traffic, crime prevention,and disasters) via CCTV images [3]. However, as CCTV is not highly effective in nighttime situations and can suffer from privacy infringement issues, CCTV-dependent systems have several shortcomings [4; 5]. A representative example is the disappearance and death of a medical student that occurred late at night on 25 April 2021, which was a big issue in Korea. Although the incident occurred in Hangang Park in the city center with a large floating population, there was no CCTV inside the park except on the access road to protect privacy, making it difficult to determine the circumstances of the incident. CCTV is also not sufficient for identifying early signs of major accidents due to temperature changes, such as fires, road status (e.g., ice and flooding), and facility conditions. Advanced control technologies combined with the recent rapidly developed cutting-edge technologies (e.g., AI, IoT, and Big Data) have been introduced to overcome CCTV's limitations. Examples include image analysis for CCTV's automatic control [6; 7; 8; 9; 10; 11; 12; 13] and integrating IoT to specifically identify situations outside the CCTV control zones [14; 15; 16]. Thermography has been particularly highlighted. It refers to images of radiation intensity measured by an infrared detector which represents the infrared thermal energy radiated from the surface of the object according to its temperature [17]. Infrared rays can be classified into near-infrared rays (NIR) with wavelengths 0.8-1.4 \(\upmu\)m, shortwave infrared rays (SWIR) with wavelengths 1.4-3 \(\upmu\)m, mid-wave infrared rays (MWIR) with wavelengths 3-5 \(\upmu\)m, long-wave infrared rays (LWIR) with wavelengths 8-14 \(\upmu\)m, and far infrared rays (VLWIR) with wavelengths 14 \(\upmu\)m or longer [18]. LWIR is the wavelength area with a maximum infrared ray for a temperature of 300 K and is the wavelength most closely related to human lives. A thermal camera that uses LWIR is a device that visualizes detected infrared thermal energy captured by a thermal detector through image processing. It enables one to confirm objects regardless of the existence or absence of lighting as it can detect radiant heat emitted by heated objects [17]. Many studies show that Thermography/IR image object recognition based on thermal cameras is superior to general RGB at night [19; 20; 21]. Indeed, such techniques have been widely applied to diverse sectors, such as traditional military purposes, night surveillance, fire disaster prevention, life rescue, maritime surveillance, traffic, and rescue. This technology especially has the advantage of real-time identification of night situations that cannot be detected by CCTV, changes in object conditions due to temperature changes, and precursor fire events (or events preceding fires). Recent thermography CCTV technology development has enabled fixed installations, and installation in mobile equipment using robots and drones.