2017/06/02 by Xuemei Xie, Chenye Wang, Shu Chen +2 · 60 citations
Computer Science · Engineering · Mathematics · #Artificial intelligence #Computer science #Computer security #Computer vision #Deep learning #Detector #Engineering #Object detection #Parking lot #Real-time computing #Region of interest #Robustness (evolution) #Segmentation #Smart Parking Systems Research #Telecommunications #Vehicle License Plate Recognition #Video Surveillance and Tracking Methods #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.1145/3094243.3094261
5pages,6figures
openalex publication_date 2017/06/02 · arxiv created 2017/10/05 · arxiv updated 2017/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.