# Deep Learning Based Video System for Accurate and Real-Time Parking Measurement

arXiv: https://arxiv.org/abs/1902.07401 · HTML: https://mlsanity.com/paper/1902.07401/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Automatic Vision-Based Parking Slot Detection and Occupancy Classification](https://mlsanity.com/paper/2308.08192/) (2023)
- [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)
- [GAN-Based Single-Stage Defense for Traffic Sign Classification Under Adversarial Patch](https://mlsanity.com/paper/2503.12567/) (2025)
- [Precision-Aware Video Compression for Reducing Bandwidth Requirements in Video Communication for Vehicle Detection-Based Applications](https://mlsanity.com/paper/2508.02533/) (2025)
- [Computer Vision-Based Vehicle Allotment System using Perspective Mapping](https://mlsanity.com/paper/2603.08827/) (2026)

## Abstract (arXiv)

Parking spaces are costly to build, parking payments are difficult to enforce, and drivers waste an excessive amount of time searching for empty lots. Accurate quantification would inform developers and municipalities in space allocation and design, while real-time measurements would provide drivers and parking enforcement with information that saves time and resources. In this paper, we propose an accurate and real-time video system for future Internet of Things (IoT) and smart cities applications. Using recent developments in deep convolutional neural networks (DCNNs) and a novel vehicle tracking filter, we combine information across multiple image frames in a video sequence to remove noise introduced by occlusions and detection failures. We demonstrate that our system achieves higher accuracy than pure image-based instance segmentation, and is comparable in performance to industry benchmark systems that utilize more expensive sensors such as radar. Furthermore, our system shows significant potential in its scalability to a city-wide scale and also in the richness of its output that goes beyond traditional binary occupancy statistics.