半教師あり回帰による空撮向け進行方向推定の汎化性能向上
Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-supervised Regression
移動する被写体の進行方向推定において、時間的連続性を教師なし信号として利用する半教師あり学習を提案し、ラベル付きデータを大幅に削減しつつ空撮タスクへの汎化性能を向上させた。
著者: Wenshan Wang, Aayush Ahuja, Yanfu Zhang, Rogerio Bonatti, Sebastian Scherer
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
