Multi-Modal Three-Stream Network for Action Recognition

--Human action recognition in video is an active yet challenging research topic due to high variation and complexity of data. In this paper, a novel video based action recognition framework utilizing complementary cues is proposed to handle this complex problem. Inspired by the successful two stream networks for action classification, additional pose features are studied and fused to enhance understanding of human action in a more abstract and semantic way. T owards practices, not only ground truth poses but also noisy estimated poses are incorporated in the framework with our proposed pre-processing module. The whole framework and each cue are evaluated on varied benchmarking datasets as JHMDB, sub-JHMDB and Penn Action. Our results outperform state-of-the-art performance on these datasets and show the strength of complementary cues. Human action recognition in video has attracted a lot of attention in varied application domains like autonomous driving, human-machine interaction, video surveillance and health support. It aims to understand human behavior and interaction by exploiting visual features and temporal dynamics from video.