Drone-based Object Counting by Spatially Regularized Regional Proposal Network
| Autor: | Yen-Liang Lin, Meng-Ru Hsieh, Winston H. Hsu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Mobile robot 02 engineering and technology 010501 environmental sciences Object (computer science) 01 natural sciences Object detection Drone Minimum bounding box 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Focus (optics) 0105 earth and related environmental sciences |
| Zdroj: | ICCV |
| DOI: | 10.48550/arxiv.1707.05972 |
| Popis: | 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. Comment: ICCV 2017 |
| Databáze: | OpenAIRE |
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