# Video-Based Parking Occupancy Detection for Smart Control System
## 5 Conclusions  
In this paper, we proposed a lightweight parking occupancy detection system. A large number of sensors could be replaced by only one embedded device. The cost of the device is low, and it can be quickly deployed on the streets. Our system detects vacant parking spots and controls streetlights, which decreases the amount of time people need to find parking spaces on streets. Our system also changes streetlight brightness according to various circumstances to reduce the potential waste of resources at night.  
For the real scene's applications, we prepared two different scenarios that could manage off- and on-street parking, with a smart control system. Tests in the real environment showed that the system works well under a wide range of light conditions. Otherwise, during the night, streetlights support the detection system, providing on-demand adaptive lighting, and making streetlights adjust their brightness based on the presence of pedestrians or cars to stay safe. This system can contribute to the creation of smart streets. The use of our intelligent system could help governments to collect potential data and to turn it into big data for future analyses of road conditions and even land management.  
This proposed parking occupancy detection system is based on object detection. We designed an object detection model using the YOLO v3 algorithm derived from MobileNet v2. This model is lightweight and can operate on the embedded system Jetson TX2. The embedded system can easily be built into existing streetlights, and one device serving many parking grids can decrease maintenance requirements. IOU and overlapping were proposed to determine parking space occupation, replacing the typical classification method. For parking occupancy detection, accuracy and recall using the CNRPark + EXT dataset reached 99% and 95%, respectively, indicating that the model can completely detect parking-space occupancy. Voting was applied to verify this system, allowing the system to detect occupancy under any condition.  
Regarding the control of streetlight brightness, object detection was used to detect vehicles on the street at night; accordingly, streetlights would enter dim mode when no vehicles were detected on the roads, thus saving energy. Experiments demonstrated that our parking occupancy detection system performs well under various weather conditions in both daytime and nighttime.  
Figure 19: Overlapping and IOU comparison. (**a**) Original image. (**b**) Overlapping area of the car in the back and the parking space. (**c**) Overlapping area of the car in the front and the parking space.  
**Author Contributions:** All authors were involved in the study presented in this manuscript. [PERSON], [PERSON] and [PERSON] determined the research framework, proposed the main idea, performed mathematical modeling, and contributed to revising and proofreading the article. [PERSON] performed algorithm implementation and simulation and wrote the original manuscript. [PERSON] reviewed the manuscript and provided scientific advice. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.