• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.321-328
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• 2023

This study is tried to to identify factors affecting academic achievement depending on the quality of class, learning immersion, level of academic achievement, and class type according to video production classes and real-time video classes in the on-contact learning situation due to the COVID-19 epidemic. The subjects of the study were 122 students enrolled in the nursing department at two universities. As a result of the study, the quality of the class was high in real-time video classes (t=-2.69, P=0.02), learning immersion was high in video production classes (t=1.14, P=0.28), and academic achievement was high in video production classes (t=4.24, P=0.01). Depending on the type of class, the effect on academic achievement is learning immersion in production video classes (β=.37, p<.001) has the most influence, and in real-time video classes, class quality (β=.29, p<.001) had the most influence on academic achievement. Based on the results of this study, it is suggested that it is necessary to develop a strategy for instructional design suitable for class types to improve academic achievement in an on-contact environment.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.3
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• pp.135-138
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• 2017

This study analyzed with the case analysis of a series of processes from job analysis survey and results analysis, and academic achievement in order to transform the curriculum of existing courses of the NCS-based video broadcasting. Also this study analysed the existing curriculum and analyzed the trend of workforce trends and needs of the broadcasting content industry. Also through a needs analysis for the industry and alumni and students, video graphics, video editing and video directing were selected. In this paper it dealt mainly with respect to the video graphics in a dual job. Modeling capability into the unit through a job analysis, animation, effects and lighting were chosen accordingly based introduction of graphics and application of graphics were derived two courses and selected profiles and performance criteria. This training according to the NCS curriculum for students was evaluated based on the student's job was to investigate the learning ability.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.890-902
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• 2021

Although the object detection accuracy with still images has been significantly improved with the advance of deep learning techniques, the object detection problem with video data remains as a challenging problem due to the real-time requirement and accuracy drop with occlusion. In this research, we propose a method in pig detection for video monitoring environment. First, we determine a motion, from a video data obtained from a tilted-down-view camera, based on the average size of each pig at each location with the training data, and extract key frames based on the motion information. For each key frame, we then apply YOLO, which is known to have a superior trade-off between accuracy and execution speed among many deep learning-based object detectors, in order to get pig's bounding boxes. Finally, we merge the bounding boxes between consecutive key frames in order to reduce false positive and negative cases. Based on the experiment results with a video data set obtained from a pig farm, we confirmed that the pigs could be detected with an accuracy of 97% at a processing speed of 37fps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.196-204
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• 2017

Behavior of intelligent video surveillance system is recognized by analyzing the pattern of the object of interest by using the frame information of video inputted from the camera and analyzes the behavior. Detection of object's certain behaviors in the crowd has become a critical problem because in the event of terror strikes. Recognition of object's certain behaviors is an important but difficult problem in the area of computer vision. As the realization of big data utilizing machine learning, data mining techniques, the amount of video through the CCTV, Smart-phone and Drone's video has increased dramatically. In this paper, we propose a multiple-sliding window method to recognize the cumulative change as one piece in order to improve the accuracy of the recognition. The experimental results demonstrated the method was robust and efficient learning units in the classification of certain behaviors.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1263-1268
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• 2018

In this paper, a video analysis is proposed to implement video surveillance system with deep learning object detection and probabilistic data association filter for tracking multiple objects, and suggests its implementation using GPU. The proposed video analysis technique involves object detection and object tracking sequentially. The deep learning network architecture uses ResNet for object detection and applies probabilistic data association filter for multiple objects tracking. The proposed video analysis technique can be used to detect intruders illegally trespassing any restricted area or to count the number of people entering a specified area. As a results of simulations and experiments, 48 channels of videos can be analyzed at a speed of about 27 fps and real-time video analysis is possible through RTSP protocol.

• The Journal of Information Systems
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• v.32 no.2
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• pp.87-108
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• 2023

Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.269-278
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• 2005

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantics-based retrieval method can be available for various queries of users. In this paper, we propose a video retrieval system which support semantics retrieval of various users for massive video data by user's keywords and comparison area learning based on automatic agent. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user becomes a query image and searches the most similar key frame through color histogram comparison and comparison area learning method that proposed. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 93 percents.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.335-343
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• 2021

The study aimed to elucidate how to design a learning environment on the premise of micro-learning (ML) and investigate its impact on website designing skills and achievement motivation among secondary school students. Adopting the experimental approach, data were collected through an achievement test, a product evaluation form, and a test to gauge motivation for achievement. The sample was divided into two experimental groups. Results revealed statistically significant differences at 0.05≥α between the mean scores of the two groups that experienced ML, irrespective of the two modes of presenting the video in the pre-test and post-test, as for the test of websites design skills, product evaluation form, and achievement motivation test. Besides, there have been statistically significant differences at 0.05≥α between the mean scores of the first experimental group that had exposure to ML using the split-video presentation style and the scores of the second experimental group that underwent ML using continuous video presentation style in the post cognitive test of website design and management skills in favor of the group that had segmented-video-presentation ML. Another salient finding is the nonexistence of significant differences at 0.05≥α between the mean scores of the first experimental group that underwent segmented-video-presentation ML and the grades of the second experimental group that received ML with continuous video presentation style in the post-application of the product scorecard of websites designing skills and the motivation test. In light of these salient findings, the study recommended using ML in teaching computer courses at different educational stages in Saudi Arabia, training computer and information technology teachers to harness ML in their teaching and using ML in designing courses at all levels of education.