scholarly journals Towards human-level performance on automatic pose estimation of infant spontaneous movements

2021 ◽  
pp. 102012
Author(s):  
Daniel Groos ◽  
Lars Adde ◽  
Ragnhild Støen ◽  
Heri Ramampiaro ◽  
Espen A.F. Ihlen
Keyword(s):  
Pose Estimation ◽  
Spontaneous Movements ◽  
Level Performance

scholarly journals The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Cristina Segalin ◽  
Jalani Williams ◽  
Tomomi Karigo ◽  
May Hui ◽  
Moriel Zelikowsky ◽  
...  
Keyword(s):  
Action Recognition ◽  
Pose Estimation ◽  
Data Extraction ◽  
Social Behaviors ◽  
Automated Analysis ◽  
Recognition System ◽  
Freely Behaving ◽  
And Behavior ◽  
User Friendly ◽  
Level Performance

The study of naturalistic social behavior requires quantification of animals’ interactions. This is generally done through manual annotation—a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS’s annotations to human annotations and find that MARS’s pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.

Predicting Entry-Level Performance Using Facet-Level, Personality-Based Employability Scales

2012 ◽  
Author(s):  
Ashley E. J. Palmer ◽  
Lauren N. Robertson ◽  
Courtney A. Nelson ◽  
Dara R. Pickering
Keyword(s):  
Entry Level ◽  
Level Performance

Trait- Versus State-Level Performance on the MMPI-2 As Assessed by the Ruff Neurobehavioral Inventor

2013 ◽  
Author(s):  
Brandon Browne ◽  
Anthony P. Andrews ◽  
Jada Stewart ◽  
Charles J. Golden
Keyword(s):  
State Level ◽  
Level Performance

Model-Based Human Pose Estimation and Its Analysis Using Hausdorff Matching

2015 ◽  
Vol 2 (2) ◽  
pp. 51 ◽  
Author(s):  
Vivek Maik ◽  
Jinho Park ◽  
Daehee Kim ◽  
Joonki Paik
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Model Based ◽  
Its Analysis ◽  
Human Pose

3D Object Recognition and Relative Localization using a 3D sensor Embedded on a Mobile Robot

2020 ◽  
Author(s):  
Gopi Krishna Erabati
Keyword(s):  
Pose Estimation ◽  
Autonomous Systems ◽  
Microsoft Kinect ◽  
Object Localization ◽  
Initial Alignment ◽  
Relative Localization ◽  
3D Data ◽  
Industry Automation ◽  
Ofthe Object ◽  
3D Descriptors

The technology in current research scenario is marching towards automation forhigher productivity with accurate and precise product development. Vision andRobotics are domains which work to create autonomous systems and are the keytechnology in quest for mass productivity. The automation in an industry canbe achieved by detecting interactive objects and estimating the pose to manipulatethem. Therefore the object localization ( i.e., pose) includes position andorientation of object, has profound ?significance. The application of object poseestimation varies from industry automation to entertainment industry and fromhealth care to surveillance. The objective of pose estimation of objects is verysigni?cant in many cases, like in order for the robots to manipulate the objects,for accurate rendering of Augmented Reality (AR) among others.This thesis tries to solve the issue of object pose estimation using 3D dataof scene acquired from 3D sensors (e.g. Kinect, Orbec Astra Pro among others).The 3D data has an advantage of independence from object texture and invarianceto illumination. The proposal is divided into two phases : An o?ine phasewhere the 3D model template of the object ( for estimation of pose) is built usingIterative Closest Point (ICP) algorithm. And an online phase where the pose ofthe object is estimated by aligning the scene to the model using ICP, providedwith an initial alignment using 3D descriptors (like Fast Point Feature Transform(FPFH)).The approach we develop is to be integrated on two di?erent platforms :1)Humanoid robot `Pyrene' which has Orbec Astra Pro 3D sensor for data acquisition,and 2)Unmanned Aerial Vehicle (UAV) which has Intel Realsense Euclidon it. The datasets of objects (like electric drill, brick, a small cylinder, cake box)are acquired using Microsoft Kinect, Orbec Astra Pro and Intel RealSense Euclidsensors to test the performance of this technique. The objects which are used totest this approach are the ones which are used by robot. This technique is testedin two scenarios, fi?rstly, when the object is on the table and secondly when theobject is held in hand by a person. The range of objects from the sensor is 0.6to 1.6m. This technique could handle occlusions of the object by hand (when wehold the object), as ICP can work even if partial object is visible in the scene.

Der Einfluss familialer Merkmale auf den Entwicklungsprozess vom Fussballtalent zum Spitzenspieler

2015 ◽  
Vol 63 (1) ◽  
Keyword(s):  
Childhood And Adolescence ◽  
Research Project ◽  
Maximum Performance ◽  
Adult Level ◽  
Strong Affinity ◽  
Youth Football ◽  
Football Team ◽  
The Family ◽  
Exogenous Factors ◽  
Level Performance

The process by which young talents develop to become top-class players once they reach the age of maximum performance is influenced by numerous factors. Among the exogenous factors, the family plays a central role. In the context of a research project carried out in cooperation with the Swiss Football Association SFV, 159 former members of the national youth football team were interviewed retrospectively, among other things concerning their family circumstances. The study is interested in understanding two issues: 1) It examines which family conditions – compared with average Swiss families – lead to success in adolescence (nomination for a national youth team). 2) Since success in adolescence by no means guarantees top-level performance at the age of maximum performance, the heterogeneity of the sample’s adult level of performance is used to compare players who later achieve greater success to the less successful players. It is found that these players come from families with many chil-dren and a strong affinity to sports. Those players who are particularly successful at the age of maximum performance also felt they received more support from their parents and siblings during childhood and adolescence than the players who went on to be less successful.

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