Detecting pedestrians and vehicles in traffic scene based on boosted HOG features and SVM

Diqing Sun, Junzo Watada

研究成果: Conference contribution

23 被引用数 (Scopus)

抄録

This paper presents a popular method called boosted hog features to detect pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we selected boosted hog features to get an satisfying result. In the part of detecting pedestrians, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good performance in various kinds of background. On that basis, we create a new feature with boosting algorithm to obtain more accurate results. In the part of detecting vehicle, we select the shadow underneath vehicle as the feature, so we can utilize it to detect vehicles in daytime. The shadow is an important feature for vehicles in traffic scenes. The region under vehicle is usually darker than other objects or backgrounds and can be segmented by setting a threshold. Finally, experimental results show that the detection of pedestrians and vehicles using boosted hog feature and linear svm combines the advantages of hog feature and adaboost classifier, and can achieve better detection results than the detector using conditional HOG features. At the end, the paper shows its efficiency and effectiveness using to application in real situations.

本文言語English
ホスト出版物のタイトルWISP 2015 - IEEE International Symposium on Intelligent Signal Processing, Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(印刷版)9781479972524
DOI
出版ステータスPublished - 2015 6月 29
外部発表はい
イベント9th IEEE International Symposium on Intelligent Signal Processing, WISP 2015 - Siena, Italy
継続期間: 2015 5月 152015 5月 17

Other

Other9th IEEE International Symposium on Intelligent Signal Processing, WISP 2015
国/地域Italy
CitySiena
Period15/5/1515/5/17

ASJC Scopus subject areas

  • 人工知能
  • 信号処理
  • 電子工学および電気工学

フィンガープリント

「Detecting pedestrians and vehicles in traffic scene based on boosted HOG features and SVM」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル