Real-Time Illegal Parking Detection System Based on Deep Learning

TL;DR

This paper presents a real-time deep learning-based system for detecting illegal parking, utilizing an optimized SSD algorithm and movement analysis to achieve high accuracy and robustness in complex environments.

Contribution

The paper introduces an optimized SSD model and a movement tracking method for improved real-time illegal parking detection.

Findings

99% detection accuracy

Real-time processing at 25 FPS

Robust performance in complex environments

Abstract

The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detection system. Illegal vehicles captured by camera are firstly located and classified by the famous Single Shot MultiBox Detector (SSD) algorithm. To improve the performance, we propose to optimize SSD by adjusting the aspect ratio of default box to accommodate with our dataset better. After that, a tracking and analysis of movement is adopted to judge the illegal vehicles in the region of interest (ROI). Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.