  Detection of moving objects is a very important task in autonomous driving
systems. After the perception phase, motion planning is typically performed in
Bird's Eye View (BEV) space. This would require projection of objects detected
on the image plane to top view BEV plane. Such a projection is prone to errors
due to lack of depth information and noisy mapping in far away areas. CNNs can
leverage the global context in the scene to project better. In this work, we
explore end-to-end Moving Object Detection (MOD) on the BEV map directly using
monocular images as input. To the best of our knowledge, such a dataset does
not exist and we create an extended KITTI-raw dataset consisting of 12.9k
images with annotations of moving object masks in BEV space for five classes.
The dataset is intended to be used for class agnostic motion cue based object
detection and classes are provided as meta-data for better tuning. We design
and implement a two-stream RGB and optical flow fusion architecture which
outputs motion segmentation directly in BEV space. We compare it with inverse
perspective mapping of state-of-the-art motion segmentation predictions on the
image plane. We observe a significant improvement of 13% in mIoU using the
simple baseline implementation. This demonstrates the ability to directly learn
motion segmentation output in BEV space. Qualitative results of our baseline
and the dataset annotations can be found in
https://sites.google.com/view/bev-modnet.
