Abstract
Hyperparameters are numerical presets whose values
are assigned prior to the commencement of the learning
process. Selecting appropriate hyperparameters is critical
for the accuracy of tracking algorithms, yet it is difficult to
determine their optimal values, in particular, adaptive ones
for each specific video sequence. Most hyperparameter optimization algorithms depend on searching a generic range
and they are imposed blindly on all sequences. Here, we
propose a novel hyperparameter optimization method that
can find optimal hyperparameters for a given sequence using an action-prediction network leveraged on Continuous
Deep Q-Learning. Since the common state-spaces for object tracking tasks are significantly more complex than the
ones in traditional control problems, existing Continuous
Deep Q-Learning algorithms cannot be directly applied. To
overcome this challenge, we introduce an efficient heuristic to accelerate the convergence behavior. We evaluate our
method on several tracking benchmarks and demonstrate its
superior performance