  We introduce a one-shot learning approach for video object tracking. The
proposed algorithm requires seeing the object to be tracked only once, and
employs an external memory to store and remember the evolving features of the
foreground object as well as backgrounds over time during tracking. With the
relevant memory retrieved and updated in each tracking, our tracking model is
capable of maintaining long-term memory of the object, and thus can naturally
deal with hard tracking scenarios including partial and total occlusion, motion
changes and large scale and shape variations. In our experiments we use the
ImageNet ILSVRC2015 video detection dataset to train and use the VOT-2016
benchmark to test and compare our Memory-Augmented Video Object Tracking
(MAVOT) model. From the results, we conclude that given its oneshot property
and simplicity in design, MAVOT is an attractive approach in visual tracking
because it shows good performance on VOT-2016 benchmark and is among the top 5
performers in accuracy and robustness in occlusion, motion changes and empty
target.
