Domain Adaptive Action Recognition with Integrated Self-Training and Feature Selection

This paper presents a domain adaptive action recognition approach, which utilizes labeled training videos taken under one environment (source domain) to train an action classifier for the videos taken under another environment (target domain), so that the cost for preparing training data can be greatly alleviated. Our proposed approach jointly utilizes self-training and feature selecting to gradually select these training data and feature dimensions that contribute to the training in target domain. With the proposed approach, classifiers for videos in new environments can be learned efficiently without extra labeling efforts. The superiority of our approach has been confirmed by multiple benchmark dataset.