# Using Deep Neural Networks to Quantify Parking Dwell Time

arXiv: https://arxiv.org/abs/2411.00158 · HTML: https://mlsanity.com/paper/2411.00158/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [Deep Learning for Visual Tracking: A Comprehensive Survey](https://mlsanity.com/paper/1912.00535/) (2019)
- [A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets](https://mlsanity.com/paper/2203.06463/) (2022)
- [Vehicle Occurrence-based Parking Space Detection](https://mlsanity.com/paper/2306.09940/) (2023)
- [Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems](https://mlsanity.com/paper/2309.16495/) (2023)
- [Automatic Vision-Based Parking Slot Detection and Occupancy Classification](https://mlsanity.com/paper/2308.08192/) (2023)

## Built on by (most cited first, 2 of 2)

- [TREK: Distill to Explore, Reinforce to Refine](https://mlsanity.com/paper/2607.05339/) (2026)
- [Maintaining Difficulty: A Margin Scheduler for Triplet Loss in Siamese Networks Training](https://mlsanity.com/paper/2603.26389/) (2026)

## Abstract (arXiv)

In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transportation solutions. However, automatically determining individual car dwell times from images faces challenges, such as images collected from low-resolution cameras, lighting variations, and weather effects. In this work, we propose a method that combines two deep neural networks to compute the dwell time of each car in a parking lot. The proposed method first defines the parking space status between occupied and empty using a deep classification network. Then, it uses a Siamese network to check if the parked car is the same as the previous image. Using an experimental protocol that focuses on a cross-dataset scenario, we show that if a perfect classifier is used, the proposed system generates 75% of perfect dwell time predictions, where the predicted value matched exactly the time the car stayed parked. Nevertheless, our experiments show a drop in prediction quality when a real-world classifier is used to predict the parking space statuses, reaching 49% of perfect predictions, showing that the proposed Siamese network is promising but impacted by the quality of the classifier used at the beginning of the pipeline.