The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Recognizing continuous action composition in human behavior is an important and yet challenging problem. In this paper we tackle the task by developing both reliable image features and classification algorithms. For image features, we introduce the Embedded Optical Flow (EOF) feature based on embedding optical flow using Locality-constrained Linear Coding with weighted average pooling. The EOF feature...
This paper introduces a probabilistic graphical model for continuous action recognition with two novel components: substructure transition model and discriminative boundary model. The first component encodes the sparse and global temporal transition prior between action primitives in state-space model to handle the large spatial-temporal variations within an action class. The second component enforces...
This paper introduces a dangerous-driving warning system that uses statistical modeling to predict driving risks. The major challenge of the research is how to discover the safe/dangerous driving patterns from a sparsely labeled training data set. This paper proposes a semisupervised learning method to utilize both the labeled and the unlabeled data, as well as their interdependence to build a proper...
Semantic video content extraction and selection are critical steps in sports video analysis and editing. The identification of video segments can be from various semantic perspectives, e.g. certain event, player or emotional state. In this paper, we examined the possibility of automatically identifying shots with "happy" or "sad" emotion from broadcast sports video. Our proposed...
Building a generic content-based sports video analysis system remains a challenging problem because of the diversity in sports rules and game features which makes it difficult to discover generic low-level features or high-level modeling algorithms. One possible alternative is to first classify the sports genre and then apply specific sports domain knowledge to perform analysis. In this paper we describe...
Sports video highlight detection is a popular topic. A multi-layer sport event detection framework is described. In the mid-level of this framework, visual and audio keywords are created from low-level features and the original video is converted into a keyword sequence. In the high-level, the temporal pattern of keyword sequences is analyzed by an HMM classifier. The creation of visual and audio...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.