Comparison of object detection schemes using datasets of sports scenes

被引：0
作者：
Miyamoto R. [1 ]
Nakamura Y. [2 ]
Ishida H. [1 ]
Nakamura T. [2 ]
Oki T. [1 ]
机构：
[1] Department of Computer Science, School of Science and Technology, Meiji University
[2] Department of Computer Science, Graduate School of Science and Technology, Meiji University
来源：
Journal of the Institute of Image Electronics Engineers of Japan | 2019年 / 48卷 / 01期
关键词：
Accuracy comparison; Deep learning; Informed-filters; Object detection;
D O I：
10.11371/iieej.48.144
中图分类号：
学科分类号：
摘要：
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. © 2019 Institute of Image Electronics Engineers of Japan. All rights reserved.
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收藏
页码：144 / 152
页数：8
相关论文
共 26 条
  • [1] Viola P., Michael J., Robust Real-Time Face Detection, International Journal of Computer Vision, 57, 2, pp. 137-154, (2004)
  • [2] Dalal N., Triggs B., Histograms of Oriented Gradients for Human Detection, Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. 886-893, (2005)
  • [3] Felzenszwalb P., Girshick R., McAllester D., Ramanan D., Object Detection with Discriminatively Trained Part Based Models, IEEE Trans. on Pattern Analysis and Machine Intelligence, 32, 9, pp. 1627-1645, (2010)
  • [4] Doll'ar P., Tu Z., Perona P., Belongie S., Integral Channel Features, Proc. of British Machine Vision Conference, pp. 1-91, (2009)
  • [5] Zhang S., Bauckhage C., Cremers A.B., Informed Haar- Like Features Improve Pedestrian Detection, Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 947-954, (2014)
  • [6] Zhang S., Benenson R., Schiele B., Filtered Channel Features for Pedestrian Detection, Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1751-1760, (2015)
  • [7] Doll'ar P., Appel R., Belongie S., Perona P., Fast Feature Pyramids for Object Detection, IEEE Trans. on Pattern Analysis and Machine Intelligence, 36, 8, pp. 1532-1545, (2014)
  • [8] Miyamoto R., Oki T., Soccer Player Detection with Only Color Features Selected Using Informed Haar-Like Features, Proc. Advanced Concepts for Intelligent Vision Systems, pp. 238-249, (2016)
  • [9] Krizhevsky A., Sutskever I., Hinton G.E., Imagenet Classification with Deep Convolutional Neural Networks, Proc. of Advances in Neural Information Processing Systems, pp. 1097-1105, (2012)
  • [10] Girshick R.B., Donahue J., Darrell T., Malik J., Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580-587, (2014)
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