# A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices

arXiv: https://arxiv.org/abs/2001.00269 · HTML: https://mlsanity.com/paper/2001.00269/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2020.

## Built on by (most cited first, 21 of 21)

- [ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility](https://mlsanity.com/paper/2409.09040/) (2024)
- [Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things](https://mlsanity.com/paper/2301.03561/) (2023)
- [SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing](https://mlsanity.com/paper/2401.16720/) (2024)
- [Few-Shot Object Detection: Research Advances and Challenges](https://mlsanity.com/paper/2404.04799/) (2024)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [Computer Vision Based Parking Optimization System](https://mlsanity.com/paper/2201.00095/) (2022)
- [A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems](https://mlsanity.com/paper/2401.06831/) (2024)
- [Automatic Vision-Based Parking Slot Detection and Occupancy Classification](https://mlsanity.com/paper/2308.08192/) (2023)
- [EdgeOL: Efficient in-situ Online Learning on Edge Devices](https://mlsanity.com/paper/2401.16694/) (2024)
- [Efficient Large-Scale Urban Parking Prediction: Graph Coarsening Based on Real-Time Parking Service Capability](https://mlsanity.com/paper/2410.04022/) (2024)
- [Image-Based Parking Space Occupancy Classification: Dataset and Baseline](https://mlsanity.com/paper/2107.12207/) (2021)
- [Parking Analytics Framework using Deep Learning](https://mlsanity.com/paper/2203.07792/) (2022)
- [Beyond Prediction: On-street Parking Recommendation using Heterogeneous Graph-based List-wise Ranking](https://mlsanity.com/paper/2305.00162/) (2023)
- [Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things](https://mlsanity.com/paper/2311.02926/) (2023)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms](https://mlsanity.com/paper/2411.11406/) (2024)
- [MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings](https://mlsanity.com/paper/2110.03302/) (2021)
- [Polymorph: Energy-Efficient Multi-Label Classification for Video Streams on Embedded Devices](https://mlsanity.com/paper/2507.14959/) (2025)
- [Edge Assisted Multi-Camera Vehicle Tracking Framework for Real-Time and Scalable Deployment](https://mlsanity.com/paper/2511.13904/) (2025)
- [Bayesian-Symbolic Integration for Uncertainty-Aware Parking Prediction](https://mlsanity.com/paper/2603.27119/) (2026)

## Abstract (arXiv)

Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprecedented speed. A traditional solution to deal with the big data would require a large amount of computing and storage resources. With the advances in Internet of things (IoT), artificial intelligence, and communication technologies, edge computing offers a new solution to the problem by processing the data partially or wholly on the edge of a surveillance system. In this study, we investigate the feasibility of using edge computing for smart parking surveillance tasks, which is a key component of Smart City. The system processing pipeline is carefully designed with the consideration of flexibility, online surveillance, data transmission, detection accuracy, and system reliability. It enables artificial intelligence at the edge by implementing an enhanced single shot multibox detector (SSD). A few more algorithms are developed on both the edge and the server targeting optimal system efficiency and accuracy. Thorough field tests were conducted in the Angle Lake parking garage for three months. The experimental results are promising that the final detection method achieves over 95% accuracy in real-world scenarios with high efficiency and reliability. The proposed smart parking surveillance system can be a solid foundation for future applications of intelligent transportation systems.