Cross-view Action Modeling, Learning and Recognition
This addresses the challenge of recognizing actions from novel views in video analysis, which is important for applications like surveillance and human-computer interaction, though it is an incremental advance in representation learning.
The paper tackles the problem of view-dependent action recognition by introducing a multiview spatio-temporal AND-OR graph (MST-AOG) representation for cross-view action recognition, enabling recognition from unseen views and achieving significant improvements in accuracy and robustness on 2D videos.
Existing methods on video-based action recognition are generally view-dependent, i.e., performing recognition from the same views seen in the training data. We present a novel multiview spatio-temporal AND-OR graph (MST-AOG) representation for cross-view action recognition, i.e., the recognition is performed on the video from an unknown and unseen view. As a compositional model, MST-AOG compactly represents the hierarchical combinatorial structures of cross-view actions by explicitly modeling the geometry, appearance and motion variations. This paper proposes effective methods to learn the structure and parameters of MST-AOG. The inference based on MST-AOG enables action recognition from novel views. The training of MST-AOG takes advantage of the 3D human skeleton data obtained from Kinect cameras to avoid annotating enormous multi-view video frames, which is error-prone and time-consuming, but the recognition does not need 3D information and is based on 2D video input. A new Multiview Action3D dataset has been created and will be released. Extensive experiments have demonstrated that this new action representation significantly improves the accuracy and robustness for cross-view action recognition on 2D videos.