  This paper studies efficient means for dealing with intra-category diversity
in object detection. Strategies for occlusion and orientation handling are
explored by learning an ensemble of detection models from visual and
geometrical clusters of object instances. An AdaBoost detection scheme is
employed with pixel lookup features for fast detection. The analysis provides
insight into the design of a robust vehicle detection system, showing promise
in terms of detection performance and orientation estimation accuracy.
