  In this paper, we propose a novel pixel-wise visual object tracking framework
that can track any anonymous object in a noisy background. The framework
consists of two submodels, a global attention model and a local segmentation
model. The global model generates a region of interests (ROI) that the object
may lie in the new frame based on the past object segmentation maps, while the
local model segments the new image in the ROI. Each model uses a LSTM structure
to model the temporal dynamics of the motion and appearance, respectively. To
circumvent the dependency of the training data between the two models, we use
an iterative update strategy. Once the models are trained, there is no need to
refine them to track specific objects, making our method efficient compared to
online learning approaches. We demonstrate our real time pixel-wise object
tracking framework on a challenging VOT dataset
