  We present an online visual tracking algorithm by managing multiple target
appearance models in a tree structure. The proposed algorithm employs
Convolutional Neural Networks (CNNs) to represent target appearances, where
multiple CNNs collaborate to estimate target states and determine the desirable
paths for online model updates in the tree. By maintaining multiple CNNs in
diverse branches of tree structure, it is convenient to deal with
multi-modality in target appearances and preserve model reliability through
smooth updates along tree paths. Since multiple CNNs share all parameters in
convolutional layers, it takes advantage of multiple models with little extra
cost by saving memory space and avoiding redundant network evaluations. The
final target state is estimated by sampling target candidates around the state
in the previous frame and identifying the best sample in terms of a weighted
average score from a set of active CNNs. Our algorithm illustrates outstanding
performance compared to the state-of-the-art techniques in challenging datasets
such as online tracking benchmark and visual object tracking challenge.
