Semantic Motion Segmentation Using Optical Flow and Convolutional Neural Networks (CNNs)
The literature for motion segmentation of image sequences is fairly large and growing.
The recent works indicate the importance of using image sequences captured using a
static or a moving camera which influence the performance of such motion segmentation
algorithms. However, motion segmentation can become complex for sequence of images
captured using a moving camera. In order to boost the performance of such motion
segmentation algorithms semantic labeling of sequence of images along with popular
motion detection techniques like optical flow have been used. In this thesis work, motion
segmentation for sequence of images captured using a moving camera is performed.
For designing this motion segmentation algorithm, a classical computer vision approach
in combination with a recent and well known deep learning approach is used. More
specifically, optical flow orientations along with convolutional neural networks (CNNs)
are used for this task. In order to obtain superior motion segmentations for moving
camera images, the thesis work uses semantic labeling for sequence of images and optical
flow orientations which are calculated using this information. Also, a CNN is used
for which these optical flow orientations are used as inputs. The performance of the
algorithm will be evaluated for multiple sequence of images.