scholarly journals Systematic Study of Video Mining with Its Applications

2021 ◽  
Author(s):  
Mallappa G. Mendagudli ◽  
K.G. Kharade ◽  
T. Nadana Ravishankar ◽  
K. Vengatesan
Keyword(s):  
Data Mining ◽  
Content Analysis ◽  
Large Datasets ◽  
Video Data ◽  
Broad Application ◽  
Medicine Research ◽  
Video Footage ◽  
Security Education ◽  
Video Mining ◽  
High Level

Effective methods for video indexing will be more valuable as digital video data continues to grow. It has been years since we’ve seen this level of new multimedia research. The content analysis aims to create high-level descriptions and annotations by treating language and facts as data. Data mining is a technique that seeks out previously unknown facts and patterns in large datasets. A video can include several different kinds of data, such as images, visuals, audio, text, and additional metadata. Thanks to its broad application in various disciplines, like security, education, medicine, research, sports, and entertainment, it is often used differently. Data mining aims to discover and articulate exciting patterns that are hidden in a lot of video footage. While video mining is still in its infancy, data mining is more mature. A considerable amount of research must be done to turn the mined video into usable content

scholarly journals Natural Language Description of Videos for Smart Surveillance

2021 ◽  
Vol 11 (9) ◽  
pp. 3730
Author(s):  
Aniqa Dilawari ◽  
Muhammad Usman Ghani Khan ◽  
Yasser D. Al-Otaibi ◽  
Zahoor-ur Rehman ◽  
Atta-ur Rahman ◽  
...  
Keyword(s):  
Natural Language ◽  
Feature Recognition ◽  
Scene Recognition ◽  
Video Data ◽  
Surveillance Video ◽  
Video Footage ◽  
Parallel Pipeline ◽  
September 11 Attacks ◽  
Description Framework ◽  
High Level

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

scholarly journals Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks

Geomatics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 34-49
Author(s):  
Mael Moreni ◽  
Jerome Theau ◽  
Samuel Foucher
Keyword(s):  
False Positive ◽  
Classification Performance ◽  
Large Datasets ◽  
False Positives ◽  
Fine Tuning ◽  
Training Time ◽  
In The Wild ◽  
Test Sets ◽  
High Level ◽  
Time Decrease

The combination of unmanned aerial vehicles (UAV) with deep learning models has the capacity to replace manned aircrafts for wildlife surveys. However, the scarcity of animals in the wild often leads to highly unbalanced, large datasets for which even a good detection method can return a large amount of false detections. Our objectives in this paper were to design a training method that would reduce training time, decrease the number of false positives and alleviate the fine-tuning effort of an image classifier in a context of animal surveys. We acquired two highly unbalanced datasets of deer images with a UAV and trained a Resnet-18 classifier using hard-negative mining and a series of recent techniques. Our method achieved sub-decimal false positive rates on two test sets (1 false positive per 19,162 and 213,312 negatives respectively), while training on small but relevant fractions of the data. The resulting training times were therefore significantly shorter than they would have been using the whole datasets. This high level of efficiency was achieved with little tuning effort and using simple techniques. We believe this parsimonious approach to dealing with highly unbalanced, large datasets could be particularly useful to projects with either limited resources or extremely large datasets.

scholarly journals Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Data ◽  
Video Analytics ◽  
Web Based ◽  
Real Time Analysis ◽  
Open Source Framework ◽  
Cluster Configuration ◽  
Time Requirements ◽  
High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

Association of the Individualizing and Binding Moral Foundations with Conceptions of Motherland in Russia

2021 ◽  
pp. 107-127
Author(s):  
O.A. Sychev ◽  
◽  
K.I. Belousov ◽  
Keyword(s):  
Content Analysis ◽  
Online Survey ◽  
Necessary Condition ◽  
Moral Foundations ◽  
Moral Norms ◽  
Positive Image ◽  
Russian Version ◽  
Moral Content ◽  
Frequent Use ◽  
High Level

Patriotism and attitude to the motherland attract much interest of researchers in the field of social sciences, although psychological foundations of these phenomena have not been sufficiently studied. On the basis of Moral Foundations Theory (by J. Haidt) we hypothesized that the characteristics of the moral sphere may be associated with some ideas about the motherland. In particular, binding moral foundations (including loyalty, authority and purity) which are more typical for conservatives should support positive patriotic ideas about the homeland. The low level of binding moral foundations can be related with a critical attitude towards the motherland, the presence of negative assessments of their country. This assump-tion is supported by the results of past studies of patriotism among individuals with different ideological views. The individualizing moral foundations dominating among liberals can be associated with a less global and narrower view of the motherland, which is linked with con-cepts of “family” and “home” rather than “society” or “country”. The aim of this study was to analyze the association between the individualizing and binding moral foundations with the content of ideas about the motherland. The important task of the study was to develop the Russian version of the moral foundations dictionary, which is necessary for analyzing the moral content of ideas about the motherland. To test the hypotheses we conducted a paper-pencil and online survey, obtained data included the answers on Moral Foundations Question-naire and textual answers characterizing the image of the motherland. The sample comprised 831 people (72% women) from 11 regions of Russia. Text responses were processed via com-puter content analysis in the LIWC program (by J. Pennebaker) using the moral foundations dictionary (all categories) and the general dictionary (10 categories most relevant to the prob-lem). To analyze the relations between the results of content analysis and MFQ questionnaire we calculated correlations of the individualizing and binding moral foundations with the presence or absence of each category in the participants’ responses. The results of the correlation analysis indicate that the individualizing moral foundations supports relatively narrow ideas about the motherland, associated with family and home, while people with a high level of binding moral foundations associate the motherland mostly with society and religion. Binding moral founda-tions support a positive image of the motherland, which is manifested in the more frequent use of positive assessments and avoidance of negative evaluations. Persons with a high level of binding moral foundations tend to ignore negative phenomena in the country, reflecting a violation of the individualizing moral norms (care and fairness). In particular, they less often mention violations of human rights and the poverty of the country's inhabitants. The necessary condition analysis revealed the effect of binding moral foundations as a necessary but insuffi-cient condition for maintaining a positive image of the motherland and the absence of negative ideas about it.

Bayes Performance of Batch Data Mining Based on Functional Dependencies

2019 ◽  
Vol 33 (03) ◽  
pp. 1959011
Author(s):  
Haixu Xi ◽  
Feiyue Ye ◽  
Sheng He ◽  
Yijun Liu ◽  
Hongfen Jiang
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Video Processing ◽  
Mining Area ◽  
Batch Processing ◽  
Video Data ◽  
Batch Processes ◽  
Functional Dependencies ◽  
Workflow System ◽  
Traffic Video

Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

ParSoDA: high-level parallel programming for social data mining

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Loris Belcastro ◽  
Fabrizio Marozzo ◽  
Domenico Talia ◽  
Paolo Trunfio
Keyword(s):  
Data Mining ◽  
Parallel Programming ◽  
Social Data ◽  
Social Data Mining ◽  
High Level

scholarly journals EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders

2021 ◽  
Vol 11 (12) ◽  
pp. 1555
Author(s):  
Gianpaolo Alvari ◽  
Luca Coviello ◽  
Cesare Furlanello
Keyword(s):  
Clinical Sample ◽  
Eye Contact ◽  
Video Camera ◽  
Autism Spectrum ◽  
Individual Characteristics ◽  
Video Data ◽  
Contact Dynamics ◽  
Treatment Programs ◽  
Robust Detection ◽  
High Level

The high level of heterogeneity in Autism Spectrum Disorder (ASD) and the lack of systematic measurements complicate predicting outcomes of early intervention and the identification of better-tailored treatment programs. Computational phenotyping may assist therapists in monitoring child behavior through quantitative measures and personalizing the intervention based on individual characteristics; still, real-world behavioral analysis is an ongoing challenge. For this purpose, we designed EYE-C, a system based on OpenPose and Gaze360 for fine-grained analysis of eye-contact episodes in unconstrained therapist-child interactions via a single video camera. The model was validated on video data varying in resolution and setting, achieving promising performance. We further tested EYE-C on a clinical sample of 62 preschoolers with ASD for spectrum stratification based on eye-contact features and age. By unsupervised clustering, three distinct sub-groups were identified, differentiated by eye-contact dynamics and a specific clinical phenotype. Overall, this study highlights the potential of Artificial Intelligence in categorizing atypical behavior and providing translational solutions that might assist clinical practice.

scholarly journals How strong is the integrity disclosure in Indonesian Province website?

2021 ◽  
Vol 3 (1) ◽  
pp. 33-44
Author(s):  
Maria Hellenikapoulos ◽  
Intiyas Utami
Keyword(s):  
Sustainable Development ◽  
Content Analysis ◽  
Local Governments ◽  
Sustainable Development Goals ◽  
Real Life ◽  
Good Image ◽  
The Public ◽  
Strategic Issue ◽  
Development Goals ◽  
High Level

The high level and trend of corruption in Indonesia Province could hinder the goal of Sustainable Development Goals point 16. This study aims to identify disclosures of integrity through websites and classify the Indonesia Provinces into 3 categories, namely high, medium, and low based on the integrity disclosure index using institutional theory. The data is based on content analysis to analyze practices through disclosure of integrity on 34 Indonesian Province websites using the Integrity Framework Disclosure Index instrument. The findings indicate that Indonesia has disclosed 775 items (48%). The items of vision, mission, and integrity report are the biggest disclosed items among other items that show Indonesia’s effort to create a “good image” in the public eyes. Several Provinces are in the moderate category because of a strategic issue in the field of education. Local governments still have to review the increase in integrity disclosure on websites and their real-life implementation to improve integrity and fight corruption in Indonesia.

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