10,156 research outputs found
Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network
For a safe, natural and effective human-robot social interaction, it is
essential to develop a system that allows a robot to demonstrate the
perceivable responsive behaviors to complex human behaviors. We introduce the
Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits
human-like social interaction skills after 14 days of interacting with people
in an uncontrolled real world. Each and every day during the 14 days, the
system gathered robot interaction experiences with people through a
hit-and-trial method and then trained the MDARQN on these experiences using
end-to-end reinforcement learning approach. The results of interaction based
learning indicate that the robot has learned to respond to complex human
behaviors in a perceivable and socially acceptable manner.Comment: 7 pages, 5 figures, accepted by IEEE-RAS ICRA'1
ELAN as flexible annotation framework for sound and image processing detectors
Annotation of digital recordings in humanities research still is, to a largeextend, a process that is performed manually. This paper describes the firstpattern recognition based software components developed in the AVATecH projectand their integration in the annotation tool ELAN. AVATecH (AdvancingVideo/Audio Technology in Humanities Research) is a project that involves twoMax Planck Institutes (Max Planck Institute for Psycholinguistics, Nijmegen,Max Planck Institute for Social Anthropology, Halle) and two FraunhoferInstitutes (Fraunhofer-Institut für Intelligente Analyse- undInformationssysteme IAIS, Sankt Augustin, Fraunhofer Heinrich-Hertz-Institute,Berlin) and that aims to develop and implement audio and video technology forsemi-automatic annotation of heterogeneous media collections as they occur inmultimedia based research. The highly diverse nature of the digital recordingsstored in the archives of both Max Planck Institutes, poses a huge challenge tomost of the existing pattern recognition solutions and is a motivation to makesuch technology available to researchers in the humanities
Lucid Data Dreaming for Video Object Segmentation
Convolutional networks reach top quality in pixel-level video object
segmentation but require a large amount of training data (1k~100k) to deliver
such results. We propose a new training strategy which achieves
state-of-the-art results across three evaluation datasets while using 20x~1000x
less annotated data than competing methods. Our approach is suitable for both
single and multiple object segmentation. Instead of using large training sets
hoping to generalize across domains, we generate in-domain training data using
the provided annotation on the first frame of each video to synthesize ("lucid
dream") plausible future video frames. In-domain per-video training data allows
us to train high quality appearance- and motion-based models, as well as tune
the post-processing stage. This approach allows to reach competitive results
even when training from only a single annotated frame, without ImageNet
pre-training. Our results indicate that using a larger training set is not
automatically better, and that for the video object segmentation task a smaller
training set that is closer to the target domain is more effective. This
changes the mindset regarding how many training samples and general
"objectness" knowledge are required for the video object segmentation task.Comment: Accepted in International Journal of Computer Vision (IJCV
DALES: Automated Tool for Detection, Annotation, Labelling and Segmentation of Multiple Objects in Multi-Camera Video Streams
In this paper, we propose a new software tool called DALES to extract semantic information
from multi-view videos based on the analysis of their visual content. Our system is fully automatic
and is well suited for multi-camera environment. Once the multi-view video sequences are
loaded into DALES, our software performs the detection, counting, and segmentation of the visual
objects evolving in the provided video streams. Then, these objects of interest are processed
in order to be labelled, and the related frames are thus annotated with the corresponding semantic
content. Moreover, a textual script is automatically generated with the video annotations.
DALES system shows excellent performance in terms of accuracy and computational speed and
is robustly designed to ensure view synchronization
A multi-modal dance corpus for research into real-time interaction between humans in online virtual environments
We present a new, freely available, multimodal corpus for research into, amongst other areas, real-time realistic interaction between humans in online virtual environments. The specific corpus scenario focuses on an online dance class application scenario where students, with avatars driven by whatever 3D capture technology are locally available to them, can learn choerographies with teacher guidance in an online virtual ballet studio. As the data corpus is focused on this scenario, it consists of student/teacher dance choreographies concurrently captured at two different sites using a variety of media modalities, including synchronised audio rigs, multiple cameras, wearable inertial measurement devices and depth sensors. In the corpus, each of the several dancers perform a number of fixed choreographies, which are both graded according to a number of specific evaluation criteria. In addition, ground-truth dance choreography annotations are provided. Furthermore, for unsynchronised sensor modalities, the corpus also includes distinctive events for data stream synchronisation. Although the data corpus is tailored specifically for an online dance class application scenario, the data is free to download and used for any research and development purposes
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