  With constant growth of civilization and modernization of cities all across
the world since past few centuries smart traffic management of vehicles is one
of the most sorted after problem by research community. It is a challenging
problem in computer vision and artificial intelligence domain. Smart traffic
management basically involves segmentation of vehicles, estimation of traffic
density and tracking of vehicles. The vehicle segmentation from traffic videos
helps realization of niche applications such as monitoring of speed and
estimation of traffic. When occlusions, background with clutters and traffic
with density variations are present, this problem becomes more intractable in
nature. Keeping this motivation in this research work, we investigate Faster
R-CNN based deep learning method towards segmentation of vehicles. This problem
is addressed in four steps viz minimization with adaptive background model,
Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result
optimization with extended topological active nets. The computational framework
uses ideas of adaptive background modeling. It also addresses shadow and
illumination related issues. Higher segmentation accuracy is achieved through
topological active net deformable models. The topological and extended
topological active nets help to achieve stated deformations. Mesh deformation
is achieved with minimization of energy. The segmentation accuracy is improved
with modified version of extended topological active net. The experimental
results demonstrate superiority of this computational framework
