Objects are often constrained to lie on a known plane. This paper conc
erns the pose determination and recognition of vehicles in traffic sce
nes, which under normal conditions stand on the ground-plane. The grou
nd-plane constraint reduces the problem of localisation and recognitio
n from 6 dof to 3 dof. The ground-plane constraint significantly reduc
es the pose redundancy of 2D image and 3D model line matches. A form o
f the generalised Hough transform is used in conjuction with explicit
probability-based voting models to find consistent matches and to iden
tify the approximate poses. The algorithms are applied to images of se
veral outdoor traffic scenes and successful results are obtained. The
work reported in this paper illustrates the efficiency and robustness
of context-based vision in a practical application of computer vision.
Multiple cameras may be used to overcome the limitations of a single
camera. Data fusion in the proposed algorithms is shown to be simple a
nd straightforward.