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Apprenticeship learning has recently attracted a wide attention due to its capability of allowing robots to learn physical tasks directly from demonstrations provided by human experts. Most previous techniques assumed that the state space is known a priori or employed simple state representations that usually suffer from perceptual aliasing. Different from previous research, we propose a novel approach...
Human activity recognition has a variety of important real-world applications, such as video analysis, surveillance, and human-robot interaction. As a promising video representation method, local spatio-temporal (LST) features have received increasing attention from computer vision, machine learning, and robotics communities. However, approaches based on traditional LST features only use color information,...
Robotic first responders have potential to significantly improve rescue efficiency and safety in search and rescue missions. To operate intelligently, a robot requires the capability to recognize critical objects in a disaster environment, in order to effectively locate victims and/or prevent secondary disasters. In this report, we introduce a novel dataset of Critical Objects for Response to Emergency...
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second...
Activity recognition of multi-individuals (ARMI) within a group, which is essential to practical human-centered robotics applications such as childhood education, is a particularly challenging and previously not well studied problem. We present a novel adaptive human-centered (AdHuC) representation based on local spatio-temporal features (LST) to address ARMI in a sequence of 3D point clouds. Our...
The Point-and-Shoot Face Recognition Challenge (PaSC) is a performance evaluation challenge including 1401 videos of 265 people acquired with handheld cameras and depicting people engaged in activities with non-frontal head pose. This report summarizes the results from a competition using this challenge problem. In the Video-to-video Experiment a person in a query video is recognized by comparing...
Recognizing human activities from common color image sequences faces many challenges, such as complex backgrounds, camera motion, and illumination changes. In this paper, we propose a new 4-dimensional (4D) local spatio-temporal feature that combines both intensity and depth information. The feature detector applies separate filters along the 3D spatial dimensions and the 1D temporal dimension to...
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