1 research outputs found
Visual Attribute-augmented Three-dimensional Convolutional Neural Network for Enhanced Human Action Recognition
Visual attributes in individual video frames, such as the presence of
characteristic objects and scenes, offer substantial information for action
recognition in videos. With individual 2D video frame as input, visual
attributes extraction could be achieved effectively and efficiently with more
sophisticated convolutional neural network than current 3D CNNs with
spatio-temporal filters, thanks to fewer parameters in 2D CNNs. In this paper,
the integration of visual attributes (including detection, encoding and
classification) into multi-stream 3D CNN is proposed for action recognition in
trimmed videos, with the proposed visual Attribute-augmented 3D CNN (A3D)
framework. The visual attribute pipeline includes an object detection network,
an attributes encoding network and a classification network. Our proposed A3D
framework achieves state-of-the-art performance on both the HMDB51 and the
UCF101 datasets