Moving Object Proposals with Deep Learned Optical Flow for Video Object
  Segmentation
Ge Shi Zhili Yang
Dynamic scene understanding is one of the most conspicuous field of interest
among computer vision community. In order to enhance dynamic scene
understanding, pixel-wise segmentation with neural networks is widely accepted.
The latest researches on pixel-wise segmentation combined semantic and motion
information and produced good performance. In this work, we propose a state of
art architecture of neural networks to accurately and efficiently get the
moving object proposals (MOP). We first train an unsupervised convolutional
neural network (UnFlow) to generate optical flow estimation. Then we render the
output of optical flow net to a fully convolutional SegNet model. The main
contribution of our work is (1) Fine-tuning the pretrained optical flow model
on the brand new DAVIS Dataset; (2) Leveraging fully convolutional neural
networks with Encoder-Decoder architecture to segment objects. We developed the
codes with TensorFlow, and executed the training and evaluation processes on an
AWS EC2 instance.