scholarly journals A distributed Content-Based Video Retrieval system for large datasets

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
El Mehdi Saoudi ◽  
Said Jai-Andaloussi
Keyword(s):  
Real Time ◽  
Video Retrieval ◽  
Video Indexing ◽  
Video Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Indexing And Retrieval ◽  
Distributed Cluster ◽  
Efficient Video ◽  
Content Based Video Retrieval

AbstractWith the rapid growth in the amount of video data, efficient video indexing and retrieval methods have become one of the most critical challenges in multimedia management. For this purpose, Content-Based Video Retrieval (CBVR) is nowadays an active area of research. In this article, a CBVR system providing similar videos from a large multimedia dataset based on query video has been proposed. This approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key frames for rapid browsing and efficient video indexing. The proposed method has been implemented on both single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments were performed using various benchmark action and activity recognition datasets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to previous studies.

scholarly journals A Distributed Content-Based Video Retrieval System for Large Data-sets

2021 ◽  
Author(s):  
ElMehdi SAOUDI ◽  
Said Jai Andaloussi
Keyword(s):  
Real Time ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Data Set ◽  
Content Based Video Retrieval

Abstract With the rapid growth of the volume of video data and the development of multimedia technologies, it has become necessary to have the ability to accurately and quickly browse and search through information stored in large multimedia databases. For this purpose, content-based video retrieval ( CBVR ) has become an active area of research over the last decade. In this paper, We propose a content-based video retrieval system providing similar videos from a large multimedia data-set based on a query video. The approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key-frames for rapid browsing and efficient video indexing. We have implemented the proposed approach on both, single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments are performed using various benchmark action and activity recognition data-sets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to state-of-the-art methods.

scholarly journals Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data

2020 ◽  
Vol 10 (18) ◽  
pp. 6270
Author(s):  
Erik Sonnleitner ◽  
Oliver Barth ◽  
Alexander Palmanshofer ◽  
Marc Kurz
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Road Traffic ◽  
Hybrid Approach ◽  
Real Life ◽  
Video Data ◽  
Machine Learning Techniques ◽  
Traffic Measurement ◽  
Learning Techniques ◽  
Toll Station

Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%.

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

Video representation and suspicious event detection using semantic technologies

Semantic Web ◽  
2020 ◽  
pp. 1-25
Author(s):  
Ashish Singh Patel ◽  
Giovanni Merlino ◽  
Dario Bruneo ◽  
Antonio Puliafito ◽  
O.P. Vyas ◽  
...  
Keyword(s):  
Event Detection ◽  
Video Data ◽  
Machine Learning Techniques ◽  
Novel Approach ◽  
Learning Techniques ◽  
Video Scene ◽  
Scene Representation ◽  
Level Information ◽  
Complex Events ◽  
Suspicious Activity

Storage and analysis of video surveillance data is a significant challenge, requiring video interpretation and event detection in the relevant context. To perform this task, the low-level features including shape, texture, and color information are extracted and represented in symbolic forms. In this work, a methodology is proposed, which extracts the salient features and properties using machine learning techniques and represent this information as Linked Data using a domain ontology that is explicitly tailored for detection of certain activities. An ontology is also developed to include concepts and properties which may be applicable in the domain of surveillance and its applications. The proposed approach is validated with actual implementation and is thus evaluated by recognizing suspicious activity in an open parking space. The suspicious activity detection is formalized through inference rules and SPARQL queries. Eventually, Semantic Web Technology has proven to be a remarkable toolchain to interpret videos, thus opening novel possibilities for video scene representation, and detection of complex events, without any human involvement. The proposed novel approach can thus have representation of frame-level information of a video in structured representation and perform event detection while reducing storage and enhancing semantically-aided retrieval of video data.

scholarly journals Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5504
Author(s):  
Hyang-A Park ◽  
Gilsung Byeon ◽  
Wanbin Son ◽  
Hyung-Chul Jo ◽  
Jongyul Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Storage System ◽  
Research Field ◽  
Optimization Techniques ◽  
Virtual Space ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Digital Twin ◽  
Learning Techniques

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

scholarly journals Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1268 ◽  
Author(s):  
Zhenzhen Di ◽  
Miao Chang ◽  
Peikun Guo ◽  
Yang Li ◽  
Yin Chang
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Real Time ◽  
Yangtze River Basin ◽  
Clustering Algorithms ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Learning Techniques ◽  
The Yangtze River Basin ◽  
Spatio Temporal

Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

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