scholarly journals Action Recognition for the Robotics and Manufacturing Automation Using 3-D Binary Micro-block Difference

2021 ◽  
Author(s):  
Viacheslav Voronin ◽  
Marina Zhdanova ◽  
Evgenii Semenishchev ◽  
Aleksander Zelenskii ◽  
Yigang Cen ◽  
...  
Keyword(s):  
Control Systems ◽  
Action Recognition ◽  
Recognition Algorithm ◽  
Working Environment ◽  
Visible Range ◽  
Data Sets ◽  
Effective Algorithm ◽  
Manufacturing Automation ◽  
Depth Sensors ◽  
Multimodal Information

Abstract Vision-based control systems play an important role in modern robotics systems. An important task in implementing such a system is the development of an effective algorithm for recognizing human actions and the working environment and the design of intuitive gesture commands. This paper proposes an action recognition algorithm for robotics and manufacturing automation. The key contributions are (1) fusion of multimodal information obtained by depth sensors and cameras of the visible range, (2) modified Gabor-based and 3-D binary-based descriptor using micro-block difference, (3) efficient skeleton-based descriptor, and (4) recognition algorithm using the combined descriptor. The proposed binary micro-block difference representation of 3-D patches from video with a complex background in several scales and orientations leads to an informative description of the scene action. The experimental results showed the effectiveness of the proposed algorithm on data sets.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

A Projection-based Human Motion Recognition Algorithm based on Depth Sensors

2021 ◽  
pp. 1-1
Author(s):  
Mu-Chun Su ◽  
Pang-Ti Tai ◽  
Jieh-Haur Chen ◽  
Yi-Zeng Hsieh ◽  
Shu-Fang Lee ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Depth Sensors ◽  
Human Motion Recognition

Intercomparison of Cloud Products based on S5P/TROPOMI Level 1b Data Version 1 and the updated Version 2

2021 ◽  
Author(s):  
Miriam Latsch ◽  
Andreas Richter ◽  
John P. Burrows ◽  
Thomas Wagner ◽  
Holger Sihler ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Oxygen Absorption ◽  
Data Sets ◽  
Absorption Bands ◽  
Data Set ◽  
Trace Species ◽  
Retrieval Scheme

<p>The first European Sentinel satellite for monitoring the composition of the Earth’s atmosphere, the Sentinel 5 Precursor (S5p), carries the TROPOspheric Monitoring Instrument (TROPOMI) to map trace species of the global atmosphere at high spatial resolution. Retrievals of tropospheric trace gas columns from satellite measurements are strongly influenced by clouds. Thus, cloud retrieval algorithms were developed and implemented in the trace gas processing chain to consider this impact.</p><p>In this study, different cloud products available for NO<sub>2</sub> retrievals based on the TROPOMI level 1b data version 1 and an updated TROPOMI level 1b test data set of version 2 (Diagnostic Data Set 2B, DDS2B) are analyzed. The data sets include a) the TROPOMI level 2 OCRA/ROCINN (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks) cloud products CRB (cloud as reflecting boundaries) and CAL (clouds as layers), b) the FRESCO (Fast Retrieval Scheme for Clouds from Oxygen absorption bands) cloud product,  c) the cloud fraction from the NO<sub>2</sub> fitting window, d) the VIIRS (Visible Infrared Imaging Radiometer Suite) cloud product, and e) the MICRU (Mainz Iterative Cloud Retrieval Utilities) cloud fraction. The cloud products are compared with regard to cloud fraction, cloud height, cloud albedo/optical thickness, flagging and quality indicators in all 4 seasons. In particular, the differences of the cloud products under difficult situations such as snow or ice cover and sun glint are investigated.</p><p>We present results of a statistical analysis on a limited data set comparing cloud products from the current and the upcoming lv2 data versions and their approaches. The aim of this study is to better understand TROPOMI cloud products and their quantitative impacts on trace gas retrievals.</p>

scholarly journals Real-Time Continuous Action Recognition Using Pose Contexts With Depth Sensors

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 51708-51720 ◽  
Author(s):  
Hejun Wu ◽  
Zhenye Huang ◽  
Biao Hu ◽  
Zhi Yu ◽  
Xiying Li ◽  
...  
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Continuous Action ◽  
Depth Sensors

Free Evaluation System Framework (Freva) - New Features and Development

2021 ◽  
Author(s):  
Etor E. Lucio-Eceiza ◽  
Christopher Kadow ◽  
Martin Bergemann ◽  
Mahesh Ramadoss ◽  
Sebastian Illing ◽  
...  
Keyword(s):  
Evaluation System ◽  
System Modeling ◽  
International Standards ◽  
Working Environment ◽  
Data Sets ◽  
Earth System ◽  
Research Projects ◽  
System Framework ◽  
The Earth ◽  
The Evaluation System

<p>The Free Evaluation System Framework (Freva - freva.met.fu-berlin.de , xces.dkrz.de , www-regiklim.dkrz.de - https://github.com/FREVA-CLINT/Freva) is a software infrastructure for standardized data and tool solutions in Earth system science. Freva runs on high performance computers (HPC) to handle customizable evaluation systems of research projects, institutes or universities. It combines different software technologies into one common hybrid infrastructure, where all its features are accessible via shell and web environment. Freva indexes different data projects into one common search environment by storing the metadata information of the self-describing model, reanalysis and observational data sets in a database. The database interface satisfies the international standards provided by the Earth System Grid Federation (ESGF). This implemented metadata system with its advanced but easy-to-handle search tool supports users, developers and their plugins to retrieve the required information. A generic application programming interface (API) allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language used. Facilitation of the provision and usage of tools and climate data automatically increases the number of scientists working with the data sets and identifying discrepancies. Plugins are also able to integrate their e.g. post-processed results into the database of the user. This allows e.g. post-processing plugins to feed statistical analysis plugins, which fosters an active exchange between plugin developers of a research project. Additionally, the history and configuration sub-system stores every analysis performed with the evaluation system in a database. Configurations and results of the tools can be shared among scientists via shell or web system. Therefore, plugged-in tools benefit from transparency and reproducibility. Furthermore, the system suggests existing results already produced by other users – saving CPU hours, I/O, disk space and time. An integrated web shell (shellinabox) adds a degree of freedom in the choice of the working environment and can be used as a gate to the research projects on a HPC. Freva efficiently frames the interaction between different technologies thus improving the Earth system modeling science. New Features and aspects of further development and collaboration are discussed.</p>

DMMs-Based Multiple Features Fusion for Human Action Recognition

2015 ◽  
Vol 6 (4) ◽  
pp. 23-39 ◽  
Author(s):  
Mohammad Farhad Bulbul ◽  
Yunsheng Jiang ◽  
Jinwen Ma
Keyword(s):  
Action Recognition ◽  
Recognition Performance ◽  
Recognition Task ◽  
Human Action Recognition ◽  
Fusion Rule ◽  
Local Binary Patterns ◽  
Human Action ◽  
Decision Fusion ◽  
Soft Decision ◽  
Depth Sensors

The emerging cost-effective depth sensors have facilitated the action recognition task significantly. In this paper, the authors address the action recognition problem using depth video sequences combining three discriminative features. More specifically, the authors generate three Depth Motion Maps (DMMs) over the entire video sequence corresponding to the front, side, and top projection views. Contourlet-based Histogram of Oriented Gradients (CT-HOG), Local Binary Patterns (LBP), and Edge Oriented Histograms (EOH) are then computed from the DMMs. To merge these features, the authors consider decision-level fusion, where a soft decision-fusion rule, Logarithmic Opinion Pool (LOGP), is used to combine the classification outcomes from multiple classifiers each with an individual set of features. Experimental results on two datasets reveal that the fusion scheme achieves superior action recognition performance over the situations when using each feature individually.

scholarly journals I3D-Shufflenet Based Human Action Recognition

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 301
Author(s):  
Guocheng Liu ◽  
Caixia Zhang ◽  
Qingyang Xu ◽  
Ruoshi Cheng ◽  
Yong Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Convolution Kernel ◽  
Histogram Of Oriented Gradients ◽  
Temporal Features ◽  
Convolution Kernels

In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the shuffle layer improves the composition of features in each channel which can promote the utilization of useful information. The Histogram of Oriented Gradients (HOG) spatial-temporal features of the object are extracted for training, which can significantly improve the ability of human action expression and reduce the calculation of feature extraction. The I3D-shufflenet is testified on the UCF101 dataset, and compared with other models. The final result shows that the I3D-shufflenet has higher accuracy than the original I3D with an accuracy of 96.4%.

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