Drone-Based Object Counting by Spatially Regularized Regional Proposal Network
Open accessAbstract
Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understanding and fine-grained classification). In addition, most of prior work mainly focus on counting objects in static environments with fixed cameras. Motivated by the advent of unmanned flying vehicles (i.e., drones), we are interested in detecting and counting objects in such dynamic environments. We propose Layout Proposal Networks (LPNs) and spatial kernels to simultaneously count and localize target objects (e.g., cars) in videos recorded by the drone. Different from the conventional region proposal methods, we leverage the spatial layout information (e.g., cars often park regularly) and introduce these spatially regularized constraints into our network to improve the localization accuracy. To evaluate our counting method, we present a new large-scale car parking lot dataset (CARPK) that contains nearly 90,000 cars captured from different parking lots. To the best of our knowledge, it is the first and the largest drone view dataset that supports object counting, and provides the bounding box annotations.
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Aerial images of car parking lots were collected in [38] for object counting.
“Concerning object counting, aerial images of car parking lots were collected in [38].”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [38]
RMSE and MAE metrics have been employed in DL-based counting frameworks.
“The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics have been employed in the DLbased counting frameworks [2], [38].”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [38]
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“The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics have been employed in the DL-based counting frameworks [2], [38].”
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- Authors
- Hsieh, M.-R., Lin, Y.-L., & Hsu, W. H.
- Affiliations
- National Taiwan University (TW)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 4165-4173
- OpenAlex
- W2738612274
- Cited by
- 547
- References
- 4
- Citations by year
- 2026: 42 · 2025: 107 · 2024: 81 · 2023: 81 · 2022: 66 · 2021: 56 · 2020: 59 · 2019: 45 · 2018: 7 · 2017: 2
- FWCI
- 9.03
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- Colorado Water Center
- Pontificia Universidade Católica do Paraná
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- object localization, drone video, spatial kernel, dynamic environments, bounding box annotation
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