# Video-Based Parking Occupancy Detection for Smart Control System
## 1 Introduction  
With the development of technology, infrastructure has been gradually improving; city infrastructure is vital for ensuring social, economic, and physical welfare. Hence, cities face considerable urban planning challenges [1]. They face rapidly growing populations as well as social and sustainability changes. Many approaches to automating and facilitating smart cities have been developed. Smart cities are typically described as complex networks formed from resource interdependencies. Recent advancements in computer vision and intelligent technologies may assist urban renewal and reduce social costs in the pursuit of improving welfare [2; 3; 4]. To improve living environments using an existing device and vision-based technology, we propose a method of vehicle occupancy detection using a streetlight assistant system. The system detects vehicles with streetlight cameras and finds empty parking spaces on roadsides to reduce the amount of time required to find available parking spaces. It uses streetlights to assist detection by automatically maintaining half brightness at night and shifting to high brightness when an object is detected. Stability is a primary factor that should be considered when selecting a detection scheme. Stable indicators should accurately provide information about parking spaces on streets, regardless of variables in the environment, such as rain, difficult angles of view, and nighttime conditions. Second, maintenance is a crucial determinant, which includes the difficulties of deploying and maintaining new tools for everyparking space. Third, the number of sensing units directly affects the establishment and maintenance expenses of detection systems. Traditional lighting systems only offer two options, i.e., on and off, which wastes energy [5]. Hence, more innovative energy-saving devices have been developed. In this regard, several automatic smart streetlight methods have been proposed for control at night using light-dependent resistor (LDR) sensors [6] to detect vehicle and human movement, switching on streetlights ahead of the object and switching them off subsequently to save energy. Common visual sensors such as infrared and obstacle avoidance sensors [7] have also been implemented. Sun tracking sensors [8] are also used to switch off streetlights by detecting sunlight luminance. [PERSON] et al. [9] presented a method using LDR sensors and image processing to detect vehicle movement. The method [10] proposed a solar energy-based and ZigBee-based system. [PERSON] et al. [11] introduced an Arduino-based automation system, which used solar rays and object detection to control streetlights through LDR and infrared sensors. In addition to on and off settings, it introduced a half brightness (dim) setting [12; 13] to control streetlight intensity by dimming or brightening the light intensity according to the detection results. Many parking guidance systems are currently on the market, but traffic congestion remains persistent everywhere. Maintaining smooth traffic conditions concerns city managers. In the parking detection literature, researchers have proposed methods that use sensors, traditional image processing, and deep learning. Sensor-based methods, such as ultrasonic sensing, inductive looping, infrared laser sensing, and magnetic signaling, are preinstalled in parking grids [14; 15] to determine occupancy status. [PERSON] et al. [16] proposed a parking-space occupancy detection system using Arduino, Raspberry Pi, and combined infrared sensors with ultrasonic sensors for exterior/interior usage to detect whether a parking space was occupied. These sensors are typically reliable; however, a disadvantage of sensor-based methods is the considerable cost of applying the technology to entire streets. Furthermore, sensor detection of parking spaces is typically applied in indoor parking lots. Each grid requires at least one sensor and needs a server device to compute the parking gird. Thus, the implementation of this on streets is unfeasible.