  Moving object Detection (MOD) is a critical task in autonomous driving as
moving agents around the ego-vehicle need to be accurately detected for safe
trajectory planning. It also enables appearance agnostic detection of objects
based on motion cues. There are geometric challenges like motion-parallax
ambiguity which makes it a difficult problem. In this work, we aim to leverage
the vehicle motion information and feed it into the model to have an adaptation
mechanism based on ego-motion. The motivation is to enable the model to
implicitly perform ego-motion compensation to improve performance. We convert
the six degrees of freedom vehicle motion into a pixel-wise tensor which can be
fed as input to the CNN model. The proposed model using Vehicle Motion Tensor
(VMT) achieves an absolute improvement of 5.6% in mIoU over the baseline
architecture. We also achieve state-of-the-art results on the public
KITTI_MoSeg_Extended dataset even compared to methods which make use of LiDAR
and additional input frames. Our model is also lightweight and runs at 85 fps
on a TitanX GPU. Qualitative results are provided in
https://youtu.be/ezbfjti-kTk.
