This paper presents the method of our submission for THUMOS15 action recognition challenge. We propose a new action recognition system by exploiting very deep two-stream ConvNets and Fisher vector representation of iDT features. Specifically, we utilize those successful very deep architectures in images such as GoogLeNet and VGGNet to design the two-stream ConvNets. From our experiments, we see that deeper architectures obtain higher performance for spatial nets. However, for temporal net, deeper archi-tectures could not yield better recognition accuracy. We an-alyze that the UCF101 dataset is relatively very small and it is very hard to train such deep networks on the current ac-tion datasets. Compared with traditional iDT features, our implemented two-stream ConvNets significantly outperfor-m them. We further combine the recognition scores of both two-stream ConvNets and iDT features, and achieve 68% mAP value on the validation dataset of THUMOS15. 1.

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