This paper instroduces an unsupervised framework to extract semantically rich features for video representation. Inspired by how the human visual system groups objects based on motion cues, we propose a deep convolutional neural network that disentangles motion, foreground and background information. The proposed architecture consists of a 3D convolutional feature encoder for blocks of 16 frames, which is trained for reconstruction tasks over the first and last frames of the sequence. The model is trained with a fraction of videos from the UCF-101 dataset taking as ground truth the bounding boxes around the activity regions. Qualitative results indicate that the network can successfully update the foreground appearance based on pure-motion features. The benefits of these learned features are shown in a discriminative classification task when compared with a random initialization of the network weights, providing a gain of accuracy above the 10%.

Find our paper on arXiv or download the PDF file. An extended abstract of this publication was accepted as poster in the CVPR 2017 Workshop on Brave new ideas for motion representations in videos II.

If you find this work useful, please consider citing:

@InProceedings{lin2017disentangling, title={Disentangling Motion, Foreground and Background Features in Videos}, author={Lin, Xunyu and Campos, Victor and Giro-i-Nieto, Xavier and Torres, Jordi and Ferrer, Cristian Canton}, booktitle = {CVPR 2017 Workshop: Brave new ideas for motion representations in videos II}, month = {July}, year={2017} }

Lin, Xunyu, Victor Campos, Xavier Giro-i-Nieto, Jordi Torres, and Cristian Canton Ferrer. “Disentangling Motion, Foreground and Background Features in Videos.” CVPR 2017 Workshop: Brave new ideas for motion representations in videos II (2017).