Pedestrian Detection in Aerial Images Using Vanishing Point Transformation and Deep Learning
| Autor: | Jen-Hui Chuang, Hua-Tsung Chen, I-Chun Liao, Ya-Ching Chang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Pedestrian detection Deep learning Feature extraction 02 engineering and technology Pascal (programming language) 010501 environmental sciences 01 natural sciences Drone Object detection 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Vanishing point business computer Aerial image 0105 earth and related environmental sciences computer.programming_language |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip.2018.8451144 |
| Popis: | Drones are well-liked nowadays. However, deep learning models for object detection still cannot have high detection rates for pedestrians in aerial images even though they already show high precision on PASCAL VOC 2007. The main challenges of aerial image analysis include: (i) the size of an object in aerial images can be very small, and (ii) the objects in aerial images are tilted outward due to perspective projection deformation, which make the pedestrians hard to recognize in aerial images. In this paper, we utilize image partition and vanishing point transformation to overcome the above challenges. Experimental results demonstrate that such pre-processing methods can indeed increase the detection rates significantly for some deep learning models. |
| Databáze: | OpenAIRE |
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