The Journal of the Institute of Image Electronics Engineers of Japan
Online ISSN : 1348-0316
Print ISSN : 0285-9831
ISSN-L : 0285-9831
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  • Ryusuke MIYAMOTO, Yuta NAKAMURA, Hiroki ISHIDA, Takasumi NAKAMURA, Tak ...
    2019 Volume 48 Issue 1 Pages 144-152
    Published: 2019
    Released: June 10, 2021

    Visual object detection is one of the most difficult tasks in the field of image recognition but the detection accuracy has been drastically improved by recent machine learning techniques. Two kinds of schemes show good accuracy for object detection: detectors constructed by boosing using decision trees as weak classifiers and detectors based on deep learning. To improve the processing speed of visual object detection based on deep learning without reducing detection accuracy, YOLO adopts grid-based detection instead of sliding windows that requires huge computational costs. In this paper, the detection accuracy of Informed-Filters, Faster R-CNN, and YOLOv2 were evaluated using CG and VS-PETS2003 datasets. Based on the detection results, we discuss about the characteristics of these schemes.

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