Camera-Based Parking Slot Monitoring System using Adaptive Brightness Thresholding and HSV Color Filtering

Abstract

Traditional parking systems have been known to cause traffic queues and difficulty in locating parking spaces. This study proposes a camera-based parking slot monitoring system that combines Region of Interest (ROI), adaptive brightness thresholding, and HSV color detection, without relying on any artificial intelligence algorithm such as CNN or YOLO. This approach was chosen so that the system can run lightly in real time on a low-power embedded device such as a Raspberry Pi 4, without requiring a training dataset or GPU acceleration. A USB camera was used as the visual sensor, a Raspberry Pi 4 as the computing unit, Firebase Realtime Database as the data storage, and a web-based dashboard as the monitoring interface. A small-scale parking prototype consisting of 20 slots was tested under two lighting conditions. As a result, detection accuracy reached 87.5% under bright lighting conditions and 65% under low-light conditions, with an average data update latency of 1–2 seconds. These findings indicate that a rule-based, pixel-brightness approach is sufficiently effective for small-scale parking monitoring, although performance under low-light conditions still requires improvement through additional lighting or more adaptive thresholding methods.