Enhanced Video Segmentation with Object Tracking

The high efficiency and superior performance of fully convolutional network (FCN) architecture makes it a recent trend that employing FCN in video object segmentation task. While these FCN-based methods usually ignore the motion information between frames, which may lead to similar object inference or background clutter issues. To deal with these, we propose to use tracking techniques to improve the performance of video object segmentation. The proposed algorithm performs video object segmentation and tracking simultaneously in a unified framework. After that, the motion information provided by initial tracking result is used to rejecting outliers in the segmentation mask caused by background complexities, such as similar object inference or background clutter issues. In return, the final segmentation result can be used to supervise the tracking result. In this iterative way, the performances of the both tasks are enhanced. Experimental results on the challenging benchmark demonstrate the effectiveness of our proposed method.