Multimedia event detection via deep spatial-temporal neural networks
Jingyi Hou, Xinxiao Wu, Feiwu Yu, Yunde Jia · 2016
This paper proposes a novel method using deep spatial-temporal neural networks based on deep Convolutional Neural Network (CNN) for multimedia event detection. To sufficiently take advantage of the motion and appearance information of events from videos, our networks contain two branches: a temporal neural network and a spatial neural network. The temporal neural network captures motion information by Recurrent Neural Networks with the mutation of gated recurrent unit. The spatial neural network catches object information by using the deep CNN, to encode the CNN features as a bag of semantics with more discriminative representations. Both the temporal and spatial features are beneficial for event detection in a fully coupled way. Finally, we employ the generalized multiple kernel learning method to effectively fuse these two types of heterogeneous and complementary features for action recognition. Experiments on TRECVID MEDTest 14 dataset show that our method achieves better performance than the state of the art.