  In the task of Autonomous aerial filming of a moving actor (e.g. a person or
a vehicle), it is crucial to have a good heading direction estimation for the
actor from the visual input. However, the models obtained in other similar
tasks, such as pedestrian collision risk analysis and human-robot interaction,
are very difficult to generalize to the aerial filming task, because of the
difference in data distributions. Towards improving generalization with less
amount of labeled data, this paper presents a semi-supervised algorithm for
heading direction estimation problem. We utilize temporal continuity as the
unsupervised signal to regularize the model and achieve better generalization
ability. This semi-supervised algorithm is applied to both training and testing
phases, which increases the testing performance by a large margin. We show that
by leveraging unlabeled sequences, the amount of labeled data required can be
significantly reduced. We also discuss several important details on improving
the performance by balancing labeled and unlabeled loss, and making good
combinations. Experimental results show that our approach robustly outputs the
heading direction for different types of actor. The aesthetic value of the
video is also improved in the aerial filming task.
