TY  - CONF
AU  - Muhammad Anwer Rao
AU  - Fahad Shahbaz Khan
AU  - Joost Van de Weijer
AU  - Jorma Laaksonen
A2  - SCIA
PY  - 2017//
TI  - Top-Down Deep Appearance Attention for Action Recognition
T2  - LNCS
BT  - 20th Scandinavian Conference on Image Analysis
SP  - 297
EP  - 309
VL  - 10269
KW  - Action recognition
KW  - CNNs
KW  - Feature fusion
N2  - Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches.
L1  - http://refbase.cvc.uab.es/files/RKW2017b.pdf
N1  - LAMP; 600.109; 600.068; 600.120
ID  - Muhammad Anwer Rao2017
ER  -