A Novel Model for Human Activity Representation and Classification Based on Trajectory

In this paper, a novel model, HDP-HMM-SCFG is proposed for representing and classifying activities based on motion trajectories. In the model, activities are represented by stochastic grammar using trajectory, where trajectory segments are considered as observations emitted by the grammar terminals attached with HMMs. Then, by replacing the Euclidian distance in the kernel function of Gaussian radial radix with EMD-DTW, which is proposed to measure the distance between two trajectories by integrating the pros. of both EMD and DTW, multi-class SVM classifier is constructed. Experiments on ASL dataset are carried on to validate our approach.

Gerard Manley Hopkins' sprung rhythm: corpus study and stochastic grammar

Sprung rhythm is a complex poetic metre invented and used by Gerard Manley Hopkins. We re-examine and amplify a seminal analysis of this metre by Kiparsky (1989). We coded the sprung rhythm corpus for stress, weight and phrasing, then used a computer program to locate every scansion compatible with Kiparsky's analysis. The analysis appears to be nearly exceptionless. However, it is incomplete in that it permits dozens or even hundreds of scansions for certain lines. We propose a Parsability Principle for metrics mandating that ambiguity of scansion be minimised, and suggest that under this proposal, the Kiparskyan system is not a possible metre. Our own revised analysis adds ten new constraints and is cast in the form of a stochastic maxent grammar. It produces an acceptably low level of ambiguity in metrical parsing, and is supported by a good match to the diacritics Hopkins employed to mark his intended scansion.