BranchOut: Regularization for Online Ensemble Tracking
with Convolutional Neural Networks
Abstract
We propose an extremely simple but effective regularization technique of convolutional neural networks (CNNs),
referred to as BranchOut, for online ensemble tracking.
Our algorithm employs a CNN for target representation,
which has a common convolutional layers but has multiple branches of fully connected layers. For better regularization, a subset of branches in the CNN are selected
randomly for online learning whenever target appearance
models need to be updated. Each branch may have a different number of layers to maintain variable abstraction levels
of target appearances. BranchOut with multi-level target
representation allows us to learn robust target appearance
models with diversity and handle various challenges in visual tracking problem effectively. The proposed algorithm is
evaluated in standard tracking benchmarks and shows the
state-of-the-art performance even without additional pretraining on external tracking sequences