FODVid: Flow-guided Object Discovery in Videos
Silky Singh Shripad Deshmukh Mausoom Sarkar Rishabh Jain Mayur Hemani Balaji Krishnamurthy
Segmentation of objects in a video is challenging due to the nuances such as
motion blurring, parallax, occlusions, changes in illumination, etc. Instead of
addressing these nuances separately, we focus on building a generalizable
solution that avoids overfitting to the individual intricacies. Such a solution
would also help us save enormous resources involved in human annotation of
video corpora. To solve Video Object Segmentation (VOS) in an unsupervised
setting, we propose a new pipeline (FODVid) based on the idea of guiding
segmentation outputs using flow-guided graph-cut and temporal consistency.
Basically, we design a segmentation model incorporating intra-frame appearance
and flow similarities, and inter-frame temporal continuation of the objects
under consideration. We perform an extensive experimental analysis of our
straightforward methodology on the standard DAVIS16 video benchmark. Though
simple, our approach produces results comparable (within a range of ~2 mIoU) to
the existing top approaches in unsupervised VOS. The simplicity and
effectiveness of our technique opens up new avenues for research in the video
domain.