Drone-based Object Counting by Spatially Regularized Regional Proposal Networks
| Autor: | Meng-Ru Hsieh, 謝孟儒 |
|---|---|
| Rok vydání: | 2017 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 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. Our contributions include: 1.To our knowledge, this is the first work that leverages spatial layout information for object region proposal. We improve the average recall of the state-of-the-art region proposal methods (i.e., 59.9% to 62.5%) on a public PUCPR dataset. 2.We introduce a new large-scale car parking lot dataset (CARPK) that contains nearly 90,000 cars in drone-based high resolution images recorded from the diverse scenes of parking lots. Most important of all, compared to other parking lot datasets, our CARPK dataset is the first and the largest dataset of parking lots that can support counting. 3.We provide in-depth analyses for different decision choices of our region proposal method, and demonstrate that utilizing layout information can considerably reduce the proposals and improve the counting results. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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