  Moving object segmentation is a crucial task for autonomous vehicles as it
can be used to segment objects in a class agnostic manner based on their motion
cues. It enables the detection of unseen objects during training (e.g., moose
or a construction truck) based on their motion and independent of their
appearance. Although pixel-wise motion segmentation has been studied in
autonomous driving literature, it has been rarely addressed at the instance
level, which would help separate connected segments of moving objects leading
to better trajectory planning. As the main issue is the lack of large public
datasets, we create a new InstanceMotSeg dataset comprising of 12.9K samples
improving upon our KITTIMoSeg dataset. In addition to providing instance level
annotations, we have added 4 additional classes which is crucial for studying
class agnostic motion segmentation. We adapt YOLACT and implement a
motion-based class agnostic instance segmentation model which would act as a
baseline for the dataset. We also extend it to an efficient multi-task model
which additionally provides semantic instance segmentation sharing the encoder.
The model then learns separate prototype coefficients within the class agnostic
and semantic heads providing two independent paths of object detection for
redundant safety. To obtain real-time performance, we study different efficient
encoders and obtain 39 fps on a Titan Xp GPU using MobileNetV2 with an
improvement of 10% mAP relative to the baseline. Our model improves the
previous state of the art motion segmentation method by 3.3%. The dataset and
qualitative results video are shared in our website at
https://sites.google.com/view/instancemotseg/.

Computer Vision and Pattern Recognition;Robotics