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arXiv · 1704.03503

UC Merced Submission to the ActivityNet Challenge 2016

Yi Zhu·Shawn Newsam·Zaikun Xu
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Abstract

This notebook paper describes our system for the untrimmed classification task in the ActivityNet challenge 2016. We investigate multiple state-of-the-art approaches for action recognition in long, untrimmed videos. We exploit hand-crafted motion boundary histogram features as well feature activations from deep networks such as VGG16, GoogLeNet, and C3D. These features are separately fed to linear, one-versus-rest support vector machine classifiers to produce confidence scores for each action class. These predictions are then fused along with the softmax scores of the recent ultra-deep ResNet-101 using weighted averaging.

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Yi Zhu, Shawn Newsam, Zaikun Xu. 2017-04-11. UC Merced Submission to the ActivityNet Challenge 2016. https://arxiv.org/abs/1704.03503

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