  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.
