  Given the rising urban population and the consequential rise in traffic
congestion, the implementation of smart parking systems has emerged as a
critical matter of concern. Smart parking solutions use cameras, sensors, and
algorithms like computer vision to find available parking spaces. This method
improves parking place recognition, reduces traffic and pollution, and
optimizes travel time. In recent years, computer vision-based approaches have
been widely used. However, most existing studies rely on manually labeled
parking spots, which has implications for the cost and practicality of
implementation. To solve this problem, we propose a novel approach PakLoc,
which automatically localize parking spots. Furthermore, we present the PakSke
module, which automatically adjust the rotation and the size of detected
bounding box. The efficacy of our proposed methodology on the PKLot dataset
results in a significant reduction in human labor of 94.25\%. Another
fundamental aspect of a smart parking system is its capacity to accurately
determine and indicate the state of parking spots within a parking lot. The
conventional approach involves employing classification techniques to forecast
the condition of parking spots based on the bounding boxes derived from
manually labeled grids. In this study, we provide a novel approach called
PakSta for identifying the state of parking spots automatically. Our method
utilizes object detector from PakLoc to simultaneously determine the occupancy
status of all parking lots within a video frame. Our proposed method PakSta
exhibits a competitive performance on the PKLot dataset when compared to other
classification methods.
