5,975 research outputs found
Discovery and recognition of motion primitives in human activities
We present a novel framework for the automatic discovery and recognition of
motion primitives in videos of human activities. Given the 3D pose of a human
in a video, human motion primitives are discovered by optimizing the `motion
flux', a quantity which captures the motion variation of a group of skeletal
joints. A normalization of the primitives is proposed in order to make them
invariant with respect to a subject anatomical variations and data sampling
rate. The discovered primitives are unknown and unlabeled and are
unsupervisedly collected into classes via a hierarchical non-parametric Bayes
mixture model. Once classes are determined and labeled they are further
analyzed for establishing models for recognizing discovered primitives. Each
primitive model is defined by a set of learned parameters.
Given new video data and given the estimated pose of the subject appearing on
the video, the motion is segmented into primitives, which are recognized with a
probability given according to the parameters of the learned models.
Using our framework we build a publicly available dataset of human motion
primitives, using sequences taken from well-known motion capture datasets. We
expect that our framework, by providing an objective way for discovering and
categorizing human motion, will be a useful tool in numerous research fields
including video analysis, human inspired motion generation, learning by
demonstration, intuitive human-robot interaction, and human behavior analysis
Real-time human action recognition on an embedded, reconfigurable video processing architecture
Copyright @ 2008 Springer-Verlag.In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine (SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. “motion history image”) class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfiured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments.DTI and Broadcom Ltd
FPGA implementation of real-time human motion recognition on a reconfigurable video processing architecture
In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine(SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. ``motion history image") class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfigured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments
Flexible human-robot cooperation models for assisted shop-floor tasks
The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative
robots, i.e., robots able to work alongside and together with humans, could
bring to the whole production process. In this context, an enabling technology
yet unreached is the design of flexible robots able to deal at all levels with
humans' intrinsic variability, which is not only a necessary element for a
comfortable working experience for the person but also a precious capability
for efficiently dealing with unexpected events. In this paper, a sensing,
representation, planning and control architecture for flexible human-robot
cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable
sensors for human action recognition, AND/OR graphs for the representation of
and reasoning upon cooperation models, and a Task Priority framework to
decouple action planning from robot motion planning and control.Comment: Submitted to Mechatronics (Elsevier
DASZL: Dynamic Action Signatures for Zero-shot Learning
There are many realistic applications of activity recognition where the set
of potential activity descriptions is combinatorially large. This makes
end-to-end supervised training of a recognition system impractical as no
training set is practically able to encompass the entire label set. In this
paper, we present an approach to fine-grained recognition that models
activities as compositions of dynamic action signatures. This compositional
approach allows us to reframe fine-grained recognition as zero-shot activity
recognition, where a detector is composed "on the fly" from simple
first-principles state machines supported by deep-learned components. We
evaluate our method on the Olympic Sports and UCF101 datasets, where our model
establishes a new state of the art under multiple experimental paradigms. We
also extend this method to form a unique framework for zero-shot joint
segmentation and classification of activities in video and demonstrate the
first results in zero-shot decoding of complex action sequences on a
widely-used surgical dataset. Lastly, we show that we can use off-the-shelf
object detectors to recognize activities in completely de-novo settings with no
additional training.Comment: 10 pages, 4 figures, 3 tables, AAAI2021 submissio
MIFTel: a multimodal interactive framework based on temporal logic rules
Human-computer and multimodal interaction are increasingly used in everyday life. Machines are able to get more from the surrounding world, assisting humans in different application areas. In this context, the correct processing and management of signals provided by the environments is determinant for structuring the data. Different sources and acquisition times can be exploited for improving recognition results. On the basis of these assumptions, we are proposing a multimodal system that exploits Allen’s temporal logic combined with a prevision method. The main object is to correlate user’s events with system’s reactions. After post-elaborating coming data from different signal sources (RGB images, depth maps, sounds, proximity sensors, etc.), the system is managing the correlations between recognition/detection results and events in real-time to create an interactive environment for the user. For increasing the recognition reliability, a predictive model is also associated with the proposed method. The modularity of the system grants a full dynamic development and upgrade with custom modules. Finally, a comparison with other similar systems is shown, underlining the high flexibility and robustness of the proposed event management method
A Survey of Applications and Human Motion Recognition with Microsoft Kinect
Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation
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