"Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection

a new type-ii fuzzy logic based controller for non-linear dynamical systems with application to 3-psp parallel robot

abstract type-ii fuzzy logic has shown its superiority over traditional fuzzy logic when dealing with uncertainty. type-ii fuzzy logic controllers are however newer and more promising approaches that have been recently applied to various fields due to their significant contribution especially when the noise (as an important instance of uncertainty) emerges. during the design of type- i fuz...

A hybrid HMM/traps model for robust voice activity detection

We present three voice activity detection (VAD) algorithms that are suitable for the off-line processing of noisy speech and compare their performance on SPINE-2 evaluation data using speech recognition error rate as the quality metric. One VAD system is a simple HMM-based segmenter that uses normalized log-energy and a degree of voicing measure as raw features. The other two VAD systems focus ...

A Mobile Application to Detect Abnormal Patterns of Activity

In this paper we introduce an unsupervised online clustering algorithm to detect abnormal activities using mobile devices. This algorithm constantly monitors a user’s daily routine and builds his/her personal behavior model through online clustering. When the system observes activities that do not belong to any known normal activities, it immediately generates alert signals so that incidents ca...

Abnormal Activity Recognition Based on HDP-HMM Models

Detecting abnormal activities from sensor readings is an important research problem in activity recognition. A number of different algorithms have been proposed in the past to tackle this problem. Many of the previous state-based approaches suffer from the problem of failing to decide the appropriate number of states, which are difficult to find through a trial-and-error approach, in real-world...

Explicit State Duration HMM for Abnormality Detection in Sequences of Human Activity