  In this paper, we propose a new approach for keypoint-based object detection.
Traditional keypoint-based methods consist in classifying individual points and
using pose estimation to discard misclassifications. Since a single point
carries no relational features, such methods inherently restrict the usage of
structural information to the pose estimation phase. Therefore, the classifier
considers purely appearance-based feature vectors, thus requiring
computationally expensive feature extraction or complex probabilistic modelling
to achieve satisfactory robustness. In contrast, our approach consists in
classifying graphs of keypoints, which incorporates structural information
during the classification phase and allows the extraction of simpler feature
vectors that are naturally robust. In the present work, 3-vertices graphs have
been considered, though the methodology is general and larger order graphs may
be adopted. Successful experimental results obtained for real-time object
detection in video sequences are reported.
