mlsanityData to 14 Oct 2026

Image-Based Parking Space Occupancy Classification: Dataset and Baseline

arXiv 2107.12207 ↗First posted 26 Jul 2021cs.CVcited by 1

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Abstract (arXiv)

We introduce a new dataset for image-based parking space occupancy classification: ACPDS. Unlike in prior datasets, each image is taken from a unique view, systematically annotated, and the parking lots in the train, validation, and test sets are unique. We use this dataset to propose a simple baseline model for parking space occupancy classification, which achieves 98% accuracy on unseen parking lots, significantly outperforming existing models. We share our dataset, code, and trained models under the MIT license.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 26 Jul 2021.

Builds on

15 of 23 references.

References · 15

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3 more
  1. 2020Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learningcited by 126
  2. 2020A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devicescited by 21
  3. 2019Deep Learning Based Video System for Accurate and Real-Time Parking Measurementcited by 7

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Revising deep learning methods in parking lot occupancy detectioncited by 0