Pedestrian Recognition by Using a Dynamic Modality Selection Approach

Despite many years of research, pedestrian recognition is still a difficult, but very important task. It was proved that concatenating information from multi-modality images improves the recognition accuracy, but with a high computational cost. The authors present a modality selection approach, which is able to dynamically select the most discriminative modality for a given image and furthermore use it in the classification process. Firstly, the authors extract kernel descriptor features from a given image in three modalities: intensity, depth and flow. Secondly, the authors dynamically determine the most suitable modality for that image using both: a modality pertinence classifier and a decision confidence indicator. Thirdly, the authors classify the image in the selected modality using a linear support vector machine (SVM) approach. Numerical experiments are performed on the Daimler benchmark dataset consisting of pedestrian and non-pedestrian bounding boxes captured in outdoor urban environments and indicate that their model outperforms all the individual-modality classifiers and the model based on a posterior fusion of multi-modality decisions. Moreover, the proposed selection model is a promising and less computational expensive alternative to the concatenation of multi-modality features prior to classification.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 1862-1867
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01601926
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:24PM