  Dynamic scene understanding is a challenging problem and motion segmentation
plays a crucial role in solving it. Incorporating semantics and motion enhances
the overall perception of the dynamic scene. For applications of outdoor
robotic navigation, joint learning methods have not been extensively used for
extracting spatio-temporal features or adding different priors into the
formulation. The task becomes even more challenging without stereo information
being incorporated. This paper proposes an approach to fuse semantic features
and motion clues using CNNs, to address the problem of monocular semantic
motion segmentation. We deduce semantic and motion labels by integrating
optical flow as a constraint with semantic features into dilated convolution
network. The pipeline consists of three main stages i.e Feature extraction,
Feature amplification and Multi Scale Context Aggregation to fuse the semantics
and flow features. Our joint formulation shows significant improvements in
monocular motion segmentation over the state of the art methods on challenging
KITTI tracking dataset.
