Action Recognition In Videos
22 papers with code • 0 benchmarks • 2 datasets
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The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art.
Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room.
Most current action recognition methods heavily rely on appearance information by taking an RGB sequence of entire image regions as input.
Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information.
This paper proposes a two-stream flow-guided convolutional attention networks for action recognition in videos.