1,720,992 research outputs found
Skeleton-Based Action and Gesture Recognition for Human-Robot Collaboration
Full text linkHuman action recognition plays a major role in enabling an effective and safe collaboration between humans and robots. Considering for example a collaborative assembly task, the human worker can use gestures to communicate with the robot while the robot can exploit the recognized actions to anticipate the next steps in the assembly process, improving safety and the overall productivity. In this work, we propose a novel framework for human action recognition based on 3D pose estimation and ensemble techniques. In such framework, we first estimate the 3D coordinates of the human hands and body joints by means of OpenPose and RGB-D data. The estimated joints are then fed to a set of graph convolutional networks derived from Shift-GCN, one network for each set of joints (i.e., body, left hand and right hand). Finally, using an ensemble approach we average the output scores of all the networks to predict the final human action. The proposed framework was evaluated on a dedicated dataset, named IAS-Lab Collaborative HAR dataset, which includes both actions and gestures commonly used in human-robot collaboration tasks. The experimental results demonstrated how the ensemble of the different action recognition models helps improving the accuracy and the robustness of the overall system
Multi-view Human Parsing for Human-Robot Collaboration
Full text linkIn human-robot collaboration, perception plays a major role in enabling the robot to understand the surrounding environment and the position of humans inside the working area, which represents a key element for an effective and safe collaboration. Human pose estimators based on skeletal models are among the most popular approaches to monitor the position of humans around the robot, but they do not take into account information such as the body volume, needed by the robot for effective collision avoidance. In this paper, we propose a novel 3D human representation derived from body parts segmentation which combines high-level semantic information (i.e., human body parts) and volume information. To compute such body parts segmentation, also known as human parsing in the literature, we propose a multi-view system based on a camera network. People body parts are segmented in the frames acquired by each camera, projected into 3D world coordinates, and then aggregated to build a 3D representation of the human that is robust to occlusions. A further step of 3D data filtering has been implemented to improve robustness to outliers and segmentation accuracy. The proposed multi-view human parsing approach was tested in a real environment and its performance measured in terms of global and class accuracy on a dedicated dataset, acquired to thoroughly test the system under various conditions. The experimental results demonstrated the performance improvements that can be achieved thanks to the proposed multi-view approach
Clustering-based refinement for 3D human body parts segmentation
Full text linkA common approach to address human body parts segmentation on 3D data involves the use of a 2D segmentation network and 3D projection. Following this approach, several errors could be introduced in the final 3D segmentation output, such as segmentation errors and reprojection errors. Such errors are even more significant when considering very small body parts such as hands. In this paper, we propose a new algorithm that aims to reduce such errors and improve 3D segmentation of human body parts. The algorithm detects noise points and wrong clusters using DBSCAN algorithm, and changes the labels of the points exploiting the shape and position of the clusters. We evaluated the proposed algorithm on the 3DPeople synthetic dataset and on a real dataset, highlighting how it can greatly improve the 3D segmentation of small body parts like hands. With our algorithm we achieved an improvement up to 4.68% of IoU on the synthetic dataset and up to 2.30% of IoU in the real scenario
A Multi-view Framework for Human Parsing in Human-Robot Collaboration scenarios
Full text linkPerception plays a major role in human-robot col-
laboration tasks enabling the robot to understand the surround-
ing environment, especially the position of humans inside its
working area. This represents a key element to ensure a safe
collaboration, and several human representations have been pro-
posed in the literature (e.g., 3D bounding boxes, skeletal models).
In this work, we propose a novel 3D human representation
derived from body parts segmentation, which combines high-
level semantic information (i.e., human body parts) and volume
information. Body parts segmentation is known as human-
parsing in the literature, which mainly focuses on RGB images.
To compute our 3D human representation we propose a multi-
view system based on a camera network, where single-view body
parts segmentation masks are projected into 3D coordinates and
fused together, obtaining a 3D representation robust to occlusions.
A further step of 3D data filtering also improves robustness to
outliers. The proposed multi-view human parsing approach has
been evaluated in a real environment in terms of global and
class accuracy on a custom dataset, acquired to thoroughly test
the system under various conditions. The experimental results
demonstrate that the proposed system achieves high performance
also in multi-person scenarios where occlusions are largely
diffused
Dynamic Human-Aware Task Planner for Human-Robot Collaboration in Industrial Scenario
No full textThe collaboration between humans and robots in industrial scenarios is one of the key challenges for Industry 4.0. In particular, industrial robots offer accuracy and efficiency, while humans have experience and the capability to manage complex situations. Combining these features can enhance the industrial process by avoiding the user manipulates heavy weights and allowing him to dedicate his efforts to tasks where flexibility, quality and experience make the difference in the final product. However, the collaboration between humans and robots raises several new problems to be addressed like safety, tasks scheduling and operator ergonomics. For example, human presence in the robot workspace introduces various elements of complexity into robot planning due to its dynamism and unpredictability. Planning must take into account how to coordinate the tasks between the robot and the human and be quick in re-planning to respond reactively to the operator's trigger.
For this purpose, this work proposes a hierarchical Human-Aware Task Planner framework capable of generate a suitable plan to complete the process and manage user interrupts in order to have a constantly updated plan. The method is evaluated in a real industrial scenario and in a specific complex assembly task like the draping of carbon fiber plies
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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