Smart Camera Parking System With Auto Parking Spot Detection

TL;DR

This paper introduces PakLoc, an automatic parking spot localization method, and PakSta, a system for determining parking spot occupancy, significantly reducing manual labeling and improving accuracy in smart parking systems.

Contribution

The paper presents novel automated techniques for parking spot localization and occupancy detection, reducing manual effort and enhancing system efficiency.

Findings

94.25% reduction in human labor for parking spot localization

PakSta achieves competitive accuracy in parking occupancy detection

Proposed methods outperform manual labeling approaches

Abstract

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.