Machine Vision and Applications

, Volume 25, Issue 3, pp 599–611 | Cite as

Active learning for on-road vehicle detection: a comparative study

  • Sayanan Sivaraman
  • Mohan M. Trivedi
Special Issue Paper


In recent years, active learning has emerged as a powerful tool in building robust systems for object detection using computer vision. Indeed, active learning approaches to on-road vehicle detection have achieved impressive results. While active learning approaches for object detection have been explored and presented in the literature, few studies have been performed to comparatively assess costs and merits. In this study, we provide a cost-sensitive analysis of three popular active learning methods for on-road vehicle detection. The generality of active learning findings is demonstrated via learning experiments performed with detectors based on histogram of oriented gradient features and SVM classification (HOG–SVM), and Haar-like features and Adaboost classification (Haar–Adaboost). Experimental evaluation has been performed on static images and real-world on-road vehicle datasets. Learning approaches are assessed in terms of the time spent annotating, data required, recall, and precision.


Semi-supervised learning Active learning Annotation costs Object detection Active safety Intelligent vehicles 


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Computer Vision and Robotics Research LabUniversity of California, San DiegoLa JollaUSA

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