A direct approach for object detection with catadioptric omnidirectional cameras
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- 作者：Ibrahim Cinaroglu ; Yalin Bastanlar
- 关键词：Catadioptric omnidirectional cameras ; Object detection ; Human detection ; Car detection ; Vehicle detection
- 刊名：Signal, Image and Video Processing
- 出版年：2016
- 出版时间：February 2016
- 年：2016
- 卷：10
- 期：2
- 页码：413-420
- 全文大小：1,655 KB
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Yalin Bastanlar (1)
1. Computer Engineering Department, Izmir Institute of Technology, Izmir, Turkey - 刊物类别：Engineering
- 刊物主题：Signal,Image and Speech Processing
Image Processing and Computer Vision
Computer Imaging, Vision, Pattern Recognition and Graphics
Multimedia Information Systems - 出版者：Springer London
- ISSN：1863-1711
文摘
In this paper, we present an omnidirectional vision-based method for object detection. We first adopt the conventional camera approach that uses sliding windows and histogram of oriented gradients (HOG) features. Then, we describe how the feature extraction step of the conventional approach should be modified for a theoretically correct and effective use in omnidirectional cameras. Main steps are modification of gradient magnitudes using Riemannian metric and conversion of gradient orientations to form an omnidirectional sliding window. In this way, we perform object detection directly on the omnidirectional images without converting them to panoramic or perspective images. Our experiments, with synthetic and real images, compare the proposed approach with regular (unmodified) HOG computation on both omnidirectional and panoramic images. Results show that the proposed approach should be preferred.