• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.102-105
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• 2012

ONVIF is becoming a de factor standard specification for supporting interoperability among network video products, which also supports a specification for video analytics service. A smart IP network camera is an IP network supporting video analytics. In this paper, we present our efforts in integrating ONVIF Video Analytics Service into our currently developing smart IP network camera(SS IPNC; Soongsil Smart IP Network Camera). SSIPNC supports object detection, tracking, classification, and event detection with proprietary configuration protocol and meta data formats. SSIPNC is based on TI' IPNC ONVIF implementation which supports ONVI Core specification, and several ONVIF services such as device service, imaging service and media service, but not video analytics service.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.33-38
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• 2012

In this paper we proposed an algorithm selection method based on the features of video inputs which are acquired from the large scale integrated surveillance system. The example of integrated surveillance system is the metro railway system. Automated surveillance system at large area saves the human resource and minimizes the non-observing spots. We have analyzed the input video under this system in order to apply adequate video analytics algorithms in each installing places and for each situation. Based on the analysis, we suggested event processing scenarios and video analytic algorithm selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1146-1165
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• 2022

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.137-140
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• 2022

The article deals with increasing the effectiveness of educational content on the YouTube channel with the help of built-in analytics tools. Particular attention is paid to the creation of quality video, taking into account the reaction of end users of video materials. Ways to increase the video audience are considered. Techniques for improving video content apply to individual videos, as well as playlists and channels in general. In this case YouTube is both an independent educational platform and an auxiliary platform in a set of various educational solutions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.63-69
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• 2012

In this paper, we introduced design of intelligent surveillance camera system and typical event processing scenario for urban transit. To analyze video, we studied events that frequently occur in surveillance camera system. Event processing scenario is designed for seven representative situations(designated area intrusion, object abandon, object removal in designated area, object tracking, loitering and congestion measurement) in urban transit. Our system is optimized for low hardware complexity, real time processing and scenario dependent solution.

• Journal of KIISE
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• v.43 no.12
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• pp.1351-1355
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• 2016

The number of CCTV units is rapidly increasing annually, and the demand for intelligent video-analytics system is also increasing continuously for the effective monitoring of them. The existing analytics engines, however, require considerable computing resources and cannot provide a sufficient detection accuracy. For this paper, a light analytics engine was employed to analyze video and we collected metadata, such as an object's location and size, and the dwell time from the engine. A further data analysis was then performed to filter out the target of interest; as a result, it was possible to verify that a light engine and the heavy data analytics of the metadata from that engine can reject an enormous amount of environmental noise to extract the target of interest effectively. The result of this research is expected to contribute to the development of active intelligent-monitoring systems for the future.

• Knowledge Management Research
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• v.23 no.3
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• pp.285-311
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• 2022

In this study, based on the S-O-R framework, how individual's stimuli (i.e., video characteristics, YouTuber characteristics, real-time broadcasting characteristics of YouTube channel) form organisms (i.e., perceived usefulness, perceived pleasure, social presence), leading to viewers' responses (i.e., continuous watching intention, paid sponsorship intention) on real-time YouTube channels. For this purpose, a research model and hypotheses were constructed, and 369 questionnaire data collected from users of real-time broadcasting channel services on the YouTube platform were analyzed. Result findings confirmed that some video/YouTuber/real-time broadcasting characteristics significantly affect viewers' perceived usefulness/perceived pleasure/social presence, and further influence continuous watching/paid sponsorship intentions. Theoretical and practical implications of the findings are discussed in conclusion.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.243-250
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• 2019

In this paper, we propose a system for processing and analyzing drone image information which can be applied variously in disasters-security situation. The proposed system stores the images acquired from the drones in the server, and performs image processing and analysis according to various scenarios. According to each mission, deep-learning method is used to construct an image analysis system in the images acquired by the drone. Experiments confirm that it can be applied to traffic volume measurement, suspect and vehicle tracking, survivor identification and maritime missions.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_1
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• pp.927-939
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• 2022

This study was conducted to develop a Web-GIS based monitoring module of smart city that can effectively respond, manage and improve situation in all stages of illegal dumping management on a city scale. First, five technologies were set for the core technical elements of the module configuration. Five core technical elements are as follows; video screening technology based on motion vector analysis, human behavior detection based on intelligent video analytics technology, mobile app for receiving civil complaints about illegal dumping, illegal dumping risk model and street cleanliness map, Web-GIS based situation monitoring technology. The development contents and results for each set of core technical elements were evaluated. Finally, a Web-GIS based 'illegal dumping monitoring module' was proposed. It is possible to collect and analyze city data at the local government level through operating the proposed module. Based on this, it is able to effectively detect illegal dumpers at relatively low cost and identify the tendency of illegal dumping by systematically managing habitual occurrence areas. In the future, it is expected to be developed in the form of an add-on module of the smart city integration platform operated by local governments to ensure interoperability and scalability.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.72-83
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• 2021

The use of the predictive analytics (PA) powered by the artificial intelligence (AI) is more important in the movie sector during the COVID-19 pandemic, because Hollywood witnessed the impact of the 'Netflix Effect' and began to invest in data and AI. Our purpose is to discover a few cases of the AI centered PA in the movie industry value chain based on five objectives of PA: Compete, grow, enforce, improve, and satisfy. Even if movie companies' interest is to predict future success for competing with over-the-tops (OTTs) at a first glance, it is observed, once they start to use the PA with the AI, they try to utilize the enhanced PA platforms for remaining four objectives. As a result, ScriptBook, Vault, Pilot, Cinelytic and Merlin Video (Merlin) are use cases for the objective 'compete.' Movio of Vista Group International and Datorama of Salesforce are use cases for the objective 'grow.' Industrial Light & Magic (ILM) and Geena Davis Institute on Gender in Media (GDI) with Disney are use cases for the objective 'enforce.' Watson, Benjamin, and Greenlight Essential are use cases for the objective 'improve.' Disney Research (DR) with Simon Fraser University and California Institute of Technology is the use case for the objective 'satisfy.'