Feature selection for intelligent transportation systems

This thesis addresses the problem of feature selection for developing vision-based applications for intelligent transportation systems. The aim is to establish an efficient and robust framework for feature selection to guide the development of vision-based applications for intelligent transportation systems. To understand the correlation and mechanism between these applications fully, we categorise all of the applications into four vision-based cognitive systems, specifically, vehicle, pedestrian, driver, and traffic infrastructure. Similar to the human cognitive system, cognitive systems in intelligent transportation systems recognise objects and events based on features. Feature selection is extremely critical for achieving good performance. However, this task presents a considerable challenge because an intelligent transportation system usually contains a complicated environment, multiple objects, and an unstable background. Previous studies have indicated that feature selection is usually performed at random and without convincing reasons. To address this problem, we originally propose an efficient framework for feature selection to guide the development of cognitive systems in intelligent transportation systems. More specifically, our framework includes the scheme of maximum dependency and minimum redundancy. The proposed schemes for feature selection that are applied during the overall procedure for each system and the advantages of our feature selection schemes are shown next. Throughout this work, focussed emphasis is placed on performing a thorough performance evaluation for both the methodology and the real-world datasets. Several datasets that are used in this thesis have been made publicly available for further research in this field. Our results indicate that a significant improvement in performance is achieved by using our feature selection methods. This thesis concludes with a critical analysis of the work and an outlook for future research opportunities.

Language

  • English

Media Info

  • Pagination: 1 file

Subject/Index Terms

Filing Info

  • Accession Number: 01537369
  • Record Type: Publication
  • Source Agency: ARRB
  • Files: ATRI
  • Created Date: Sep 12 2014 9:50AM