• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.95-101
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• 2005

In this paper, some performance characteristics of multimedia traffic for mobile ad-hoc networks is studied with simulations. Synthetic streaming video is considered as the multimedia traffic for MPEG-coded video in the simulation. The synthetic video stream is generated with a video stream generation algorithm. The algorithm generate VBR traffics for MPEG video streams with special predefined GOP(group of pictures) patterns that is consisted of a sequence of I(intra-coded), P(predicted-coded) and B(bidirectional-coded) frames. The synthetic VBR streams is transmitted through UDP protocol with on-demand mobile ad-hoc network routing protocols like as AODV and DSR. And performances for video streams through mobile ad-hoc networks is evaluated, the throughputs is compared between data and video traffics.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.8
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• pp.477-485
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• 2006

In this paper, video avatar, made from live video streams captured from a real participant, was used to represent a virtual participant. By using video avatar to represent participants, the sense of reality for participants can be increased, but the correct registration is also an important issue. We configured the real and virtual cameras to have the same characteristics in order to register the video avatar. Comparing real and synthetic images, which is possible because of the similarities between real and virtual cameras, resolved registration between video avatar captured from real environment and virtual environment. The degree of incorrect registration was represented as energy, and the energy was then minimized to produce seamless registration. Experimental results show the proposed method can be used effectively for registration of video avatar.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.149-153
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• 2002

In this brief, Acoustic echo cancellation scheme is proposed to enhance the presence of multiple participants of hands-free voice and video conference. Synthetic stereo using head related transfer function and the stereo echo cancellation scheme are proposed. It is shown that the proposed synthetic stereo echo cancellation scheme is well performed by computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.782-804
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• 2011

In video-based recognition systems, stationary cameras are used to monitor an area of interest. These systems focus on a segmentation of the foreground in the video stream and the recognition of the events occurring in that area. The usual approach to discriminating the foreground from the video sequence is background subtraction. This paper presents a novel background subtraction method based on a probabilistic approach. We represent the posterior probability of the foreground based on the current image and all past images and derive an updated method. Furthermore, we present an efficient fusion method for the color and edge information in order to overcome the difficulties of existing background subtraction methods that use only color information. The suggested method is applied to synthetic data and real video streams, and its robust performance is demonstrated through experimentation.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1275-1281
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• 2011

This paper presents synthetic circumstantial judgement system that detects and predicts a fire in subway station. Unlike conventional fire surveillance systems that judge the fire or not through smoke, CO, temperature or variation of temperature, a proposed system discovers a fire more easily or gives the alarm high possibility of fire to operator through recognition of fire levels based on Fuzzy Inference System using by FCM and information of objects from video data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.8-16
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• 2014

We consider the visual barcode recognition problem in a noisy video data setup. Unlike most existing single-frame recognizers that require considerable user effort to acquire clean, motionless and blur-free barcode signals, we eliminate such extra human efforts by proposing a robust video-based barcode recognition algorithm. We deal with a sequence of noisy blurred barcode image frames by posing it as an online filtering problem. In the proposed dynamic recognition model, at each frame we infer the blur level of the frame as well as the digit class label. In contrast to a frame-by-frame based approach with heuristic majority voting scheme, the class labels and frame-wise noise levels are propagated along the frame sequences in our model, and hence we exploit all cues from noisy frames that are potentially useful for predicting the barcode label in a probabilistically reasonable sense. We also suggest a visual barcode tracking approach that efficiently localizes barcode areas in video frames. The effectiveness of the proposed approaches is demonstrated empirically on both synthetic and real data setup.

• 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 Broadcast Engineering
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• v.24 no.5
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• pp.755-764
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• 2019

Currently commercially available light field cameras are difficult to acquire 5D light field video since it can only acquire the still images or high price of the device. In order to solve these problems, we propose a deep learning based method for synthesizing the light field video from monocular video. To solve the problem of obtaining the light field video training data, we use UnrealCV to acquire synthetic light field data by realistic rendering of 3D graphic scene and use it for training. The proposed deep running framework synthesizes the light field video with each sub-aperture image (SAI) of $9{\times}9$ from the input monocular video. The proposed network consists of a network for predicting the appearance flow from the input image converted to the luminance image, and a network for predicting the optical flow between the adjacent light field video frames obtained from the appearance flow.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1282-1282
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• 2024

Given the widespread use of intelligent surveillance cameras at construction sites, recent studies have introduced vision-based deep learning approaches. These studies have focused on enhancing the performance of vision-based excavator activity recognition to automatically monitor productivity metrics such as activity time and work cycle. However, acquiring a large amount of training data, i.e., videos captured from actual construction sites, is necessary for developing a vision-based excavator activity recognition model. Yet, complexities of dynamic working environments and security concerns at construction sites pose limitations on obtaining such videos from various surveillance camera locations. Consequently, this leads to performance degradation in excavator activity recognition models, reducing the accuracy and efficiency of heavy equipment productivity analysis. To address these limitations, this study aimed to conduct sensitivity analysis of excavator activity recognition performance based on surveillance camera location, utilizing synthetic videos generated from a game-engine-based virtual environment (Unreal Engine). Various scenarios for surveillance camera placement were devised, considering horizontal distance (20m, 30m, and 50m), vertical height (3m, 6m, and 10m), and horizontal angle (0° for front view, 90° for side view, and 180° for backside view). Performance analysis employed a 3D ResNet-18 model with transfer learning, yielding approximately 90.6% accuracy. Main findings revealed that horizontal distance significantly impacted model performance. Overall accuracy decreased with increasing distance (76.8% for 20m, 60.6% for 30m, and 35.3% for 50m). Particularly, videos with a 20m horizontal distance (close distance) exhibited accuracy above 80% in most scenarios. Moreover, accuracy trends in scenarios varied with vertical height and horizontal angle. At 0° (front view), accuracy mostly decreased with increasing height, while accuracy increased at 90° (side view) with increasing height. In addition, limited feature extraction for excavator activity recognition was found at 180° (backside view) due to occlusion of the excavator's bucket and arm. Based on these results, future studies should focus on enhancing the performance of vision-based recognition models by determining optimal surveillance camera locations at construction sites, utilizing deep learning algorithms for video super resolution, and establishing large training datasets using synthetic videos generated from game-engine-based virtual environments.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.610-617
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• 2003

Similarity matching in video database is of growing importance in many new applications such as video clustering and digital video libraries. In order to provide efficient access to relevant data in large databases, there have been many research efforts in video indexing with diverse spatial and temporal features. however, most of the previous works relied on sequential matching methods or memory-based inverted file techniques, thus making them unsuitable for a large volume of video databases. In order to resolve this problem, this paper proposes an effective and scalable indexing technique using a trie, originally proposed for string matching, as an index structure. For building an index, we convert each frame into a symbol sequence using a window order heuristic and build a disk-resident trie from a set of symbol sequences. For query processing, we perform a depth-first search on the trie and execute a temporal segmentation. To verify the superiority of our approach, we perform several experiments with real and synthetic data sets. The results reveal that our approach consistently outperforms the sequential scan method, and the performance gain is maintained even with a large volume of video databases.