# Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1606.09367 · HTML: https://mlsanity.com/paper/1606.09367/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2016.

## Built on by (most cited first, 6 of 6)

- [A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets](https://mlsanity.com/paper/2203.06463/) (2022)
- [Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers](https://mlsanity.com/paper/2410.14705/) (2024)
- [Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System](https://mlsanity.com/paper/2208.08220/) (2022)
- [Smart Camera Parking System With Auto Parking Spot Detection](https://mlsanity.com/paper/2407.05469/) (2024)
- [Energy-Efficient Parking Analytics System using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.08973/) (2022)
- [Revising deep learning methods in parking lot occupancy detection](https://mlsanity.com/paper/2306.04288/) (2023)

## Abstract (arXiv)

Parking management systems, and vacancy-indication services in particular, can play a valuable role in reducing traffic and energy waste in large cities. Visual detection methods represent a cost-effective option, since they can take advantage of hardware usually already available in many parking lots, namely cameras. However, visual detection methods can be fragile and not easily generalizable. In this paper, we present a robust detection algorithm based on deep convolutional neural networks. We implemented and tested our algorithm on a large baseline dataset, and also on a set of image feeds from actual cameras already installed in parking lots. We have developed a fully functional system, from server-side image analysis to front-end user interface, to demonstrate the practicality of our method.