This paper presents two approaches for the representation and recognition of human action in video, aiming for viewpoint invariance. The paper first presents new results using a 2...
Abstract. We address the problem of learning good features for understanding video data. We introduce a model that learns latent representations of image sequences from pairs of su...
In this paper, we propose a generative model-based approach for audio-visual event classification. This approach is based on a new unsupervised learning method using an extended p...
Ming Li, Sanqing Hu, Shih-Hsi Liu, Sung Baang, Yu ...
Abstract. We propose a robust method to extract silhouettes of foreground objects from color video sequences. To cope with various changes in the background, the background is mode...
Hansung Kim, Ryuuki Sakamoto, Itaru Kitahara, Tomo...
An automatic human shape-motion analysis method based on a fusion architecture is proposed for human action recognition in videos. Robust shape-motion features are extracted from h...