Robust multi-view car detection using unsupervised sub-categorization
Cheng-Hao Kuo, Ramakant Nevatia · 2009
This paper presents a novel approach for multi-view car detection using unsupervised sub-categorization instead of manual labeling. Cars have large variability of models and the view-point makes the appearance change dramatically. For object classes with a large intra-class variation like cars, a divide-and-conquer strategy may be applied. Instead of using manually predefined intra-class sub-categorization, we examine several non-linear dimension reduction methods and group samples in the low-dimension embedding in an unsupervised way. The clustered samples have strong view-point similarities internally. A boosting-based cascade tree classifier is trained based on these sub-categorizations. To demonstrate the capability of our multi-view car detector, we create a more challenging test set with annotations. Compared to the UIUC side-view car data set, our test set contains a large range of car models, view points, and complex backgrounds. We compare our approach with previous methods and the result shows that ours outperforms the state-of-the-art methods.