  This paper presents a method for learning And-Or models to represent context
and occlusion for car detection and viewpoint estimation. The learned And-Or
model represents car-to-car context and occlusion configurations at three
levels: (i) spatially-aligned cars, (ii) single car under different occlusion
configurations, and (iii) a small number of parts. The And-Or model embeds a
grammar for representing large structural and appearance variations in a
reconfigurable hierarchy. The learning process consists of two stages in a
weakly supervised way (i.e., only bounding boxes of single cars are annotated).
Firstly, the structure of the And-Or model is learned with three components:
(a) mining multi-car contextual patterns based on layouts of annotated single
car bounding boxes, (b) mining occlusion configurations between single cars,
and (c) learning different combinations of part visibility based on car 3D CAD
simulation. The And-Or model is organized in a directed and acyclic graph which
can be inferred by Dynamic Programming. Secondly, the model parameters (for
appearance, deformation and bias) are jointly trained using Weak-Label
Structural SVM. In experiments, we test our model on four car detection
datasets --- the KITTI dataset \cite{Geiger12}, the PASCAL VOC2007 car
dataset~\cite{pascal}, and two self-collected car datasets, namely the
Street-Parking car dataset and the Parking-Lot car dataset, and three datasets
for car viewpoint estimation --- the PASCAL VOC2006 car dataset~\cite{pascal},
the 3D car dataset~\cite{savarese}, and the PASCAL3D+ car
dataset~\cite{xiang_wacv14}. Compared with state-of-the-art variants of
deformable part-based models and other methods, our model achieves significant
improvement consistently on the four detection datasets, and comparable
performance on car viewpoint estimation.
