Feature Selection Based on Real AdaBoost for Pedestrian Detection Using LIDAR

LIDAR を用いた歩行者検出のためのReal AdaBoost に基づく特徴選択

Typically, many features are utilized to detect pedestrian by using LIDAR. However, to implement a pedestrian detector based on SVM algorithm, it is necessary to manually determine the features and their parameters. In addition, there are a large number of combinations of parameters, and it is also necessary to change the parameters according to the variety of the performance of LIDAR. To overcome above problems, the authors proposed automatic selection of features by using Real AdaBoost which is a one of statistical learning techniques. In some pedestrian detection experiments, their proposal method was compare to previous method based on SVM.一般的に、LIDARを用いた歩行者検出では多数の特徴量が利用される。しかし、SVMなどを用いた識別機の構築では、利用する特徴量や特徴算出におけるパラメータは手動で決定する必要がある。そこで、統計学習手法であるReal AdaBoostを利用した識別器の構築による特徴量やパラメータの自動決定を行った。


  • English
  • Japanese

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01679682
  • Record Type: Publication
  • Source Agency: Japan Science and Technology Agency (JST)
  • Files: TRIS, JSTAGE
  • Created Date: Jul 26 2018 3:01PM