Histogram-based training initialisation of hidden Markov models for human action recognition

Human action recognition is often addressed by use of latent-state models such as the hidden Markov model and similar graphical models. As such models require Expectation-Maximisation training, arbitrary choices must be made for training initialisation, with major impact on the final recognition accuracy. In this paper, we propose a histogram-based deterministic initialisation and compare it with both random and a time-based deterministic initialisations. Experiments on a human action dataset show that the accuracy of the proposed method proved higher than that of the other tested methods. © 2010 IEEE

Deterministic initialization of hidden Markov models for human action recognition

Human action recognition is often approached in terms of probabilistic models such as the hidden Markov model or other graphical models. When learning such models by way of Expectation-Maximisation algorithms, arbitrary choices must be made for their initial parameters. Often, solutions for the selection of the initial parameters are based on random functions. However, in this paper, we argue that deterministic alternatives are preferable, and propose various methods. Experiments on a video dataset prove that the deterministic initialization is capable of achieving an accuracy that is comparable to or above the average from random initializations and suffers from no deviation thanks to its deterministic nature. The methods proposed naturally extend to be used with other graphical models such as dynamic Bayesian networks and conditional random fields. © 2009 IEEE

Erkennung menschlicher Aktivitäten zur Belehrung von Robotern

Für die Verwendung im Rahmen des Programmieren durch Vormachen-Paradigmas zur Programmierung von Robotern wurde ein Ansatz zur Klassifikation und Interpretation von menschlichen Bewegungen entwickelt. Dazu wurden erweiterte Methoden zur Beobachtung von Bewegungen untersucht und eine Prozesskette entwickelt, die unter Einsatz von Hintergrundwissen Bewegungssequenzen auf Aktivitäts-abhängig geeignete Merkmale abbildet und diese zur Erkennung von Aktivitäten nutzt