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Current video event recognition research remains largely target-centered. For real-world surveillance videos, target-centered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly...
There has been a challenging research topic on exploring an universal set of speech attributes sharing among a large number of languages for detection-based bottom-up cross-language speech recognition. In some recent research works, articulatory features are used as an universal set of speech attributes shared across many different languages. Since they are defined by human as a set of semantic articulatory...
This paper proposes a new Probabilistic Graphical Model (PGM) to incorporate the scene, event object interaction and the event temporal contexts into Dynamic Bayesian Networks (DBNs) for event recognition in surveillance videos. We first construct the event DBNs for modeling the events from their own appearance and kinematic observations, and then extend the DBN to incorporate the contexts for boosting...
The purpose of this paper is to develop an approach to learn dynamic Bayesian network (DBN) discriminatively for human activity recognition. DBN is a generative model widely used for modeling temporal events in human activity recognition. The parameters of the DBN models are usually learned through maximizing likelihood or expected likelihood. However, activity is often recognized through identifying...
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