Learn More
We present a novel latent discriminative model for human activity recognition. Unlike the approaches that require conditional independence assumptions, our model is very flexible in encoding the full connectivity among observations, latent states, and activity states. The model is able to capture richer class of contextual information in both state-state(More)
Human activity recognition is an essential task for robots to effectively and efficiently interact with the end users. Many machine learning approaches for activity recognition systems have been proposed recently. Most of these methods are built upon a strong assumption that the labels in the training data are noise-free, which is often not realistic. In(More)
Farshid Amirabdollahian1 ∗, Rieks op den Akker2 , Sandra Bedaf 3, Richard Bormann4, Heather Draper5, Vanessa Evers2, Jorge Gallego Pérez2, Gert Jan Gelderblom3, Carolina Gutierrez Ruiz 8, David Hewson8, Ninghang Hu7, Kheng Lee Koay 1, Ben Kröse7, Hagen Lehmann1, Patrizia Marti6, Hervé Michel 8, Hélène Prevot-Huille8, Ulrich Reiser 4, Joe Saunders 1 , Tom(More)
An activity recognition system is a very important component for assistant robots, but training such a system usually requires a large and correctly labeled dataset. Most of the previous works only allow training data to have a single activity label per segment, which is overly restrictive because the labels are not always certain. It is, therefore,(More)
We describe a system that recognizes human postures with heavy self-occlusion. In particular, we address posture recognition in a robot assisted-living scenario, where the environment is equipped with a top-view camera for monitoring human activities. This setup is very useful because top-view cameras lead to accurate localization and limited(More)
Video cameras are widely used for monitoring public areas, such as train stations, airports and shopping centers. When crowds are dense, automatically tracking individuals becomes a challenging task. We propose a new tracker which employs a particle filter tracking framework, where the state transition model is estimated by an optical-flow algorithm. In(More)
We describe a novel framework that combines an overhead camera and a robot RGB-D sensor for real-time people finding. Finding people is one of the most fundamental tasks in robot home care scenarios and it consists of many components, e.g. people detection, people tracking, face recognition, robot navigation. Researchers have extensively worked on these(More)