  Tracking is one of the most important but still difficult tasks in computer
vision and pattern recognition. The main difficulties in the tracking field are
appearance variation and occlusion. Most traditional tracking methods set the
parameters or templates to track target objects in advance and should be
modified accordingly. Thus, we propose a new and robust tracking method using a
Fully Convolutional Network (FCN) to obtain an object probability map and
Dynamic Programming (DP) to seek the globally optimal path through all frames
of video. Our proposed method solves the object appearance variation problem
with the use of a FCN and deals with occlusion by DP. We show that our method
is effective in tracking various single objects through video frames.
