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.

本研究提出了一种先进的神经网络结构，通过训练无监督的卷积神经网络对光流进行估计，然后将光流网络的输出渲染到完全卷积 SegNet 模型中，从而准确高效地获取运动目标提案。