• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.8 no.6
• /
• pp.36-45
• /
• 2009

Recently, there are interested in the automatic traffic flowing and accident detection using various low level information from video in the road. In this paper, the automatic traffic flowing and algorithm, and application of traffic accident detection using traffic management systems are studied. To achieve these purposes, the spatio-temporal relation models using topological and directional relations have been made, then a matching of the proposed models with the directional motion verbs proposed by Levin's verbs of inherently directed motion is applied. Finally, the synonym and antonym are inserted by using WordNet. For the similarity measuring between proposed modeling and trajectory of moving object in the video, the objects are extracted, and then compared with the trajectories of moving objects by the proposed modeling. Because of the different features with each proposed modeling, the rules that have been generated will be applied to the similarity measurement by TSR (Tangent Space Representation). Through this research, we can extend our results to the automatic accident detection of vehicle using CCTV.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.4
• /
• pp.1730-1747
• /
• 2018

Multi-view video plus depth (MVD) is a mainstream format of 3D scene representation in free viewpoint video systems. The advanced 3D extension of the high efficiency video coding (3D-HEVC) standard introduces new prediction tools to improve the coding performance of depth video. However, the depth video in 3D-HEVC is time consuming. To reduce the complexity of the depth video inter coding, we propose a fast coding unit (CU) size and mode decision algorithm. First, an off-line trained Bayesian model is built which the feature vector contains the depth levels of the corresponding spatial, temporal, and inter-component (texture-depth) neighboring largest CUs (LCUs). Then, the model is used to predict the depth level of the current LCU, and terminate the CU recursive splitting process. Finally, the CU mode search process is early terminated by making use of the mode correlation of spatial, inter-component (texture-depth), and inter-view neighboring CUs. Compared to the 3D-HEVC reference software HTM-10.0, the proposed algorithm reduces the encoding time of depth video and the total encoding time by 65.03% and 41.04% on average, respectively, with negligible quality degradation of the synthesized virtual view.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.39 no.4
• /
• pp.388-398
• /
• 2002

Video representation through representative frames (key frames) has been addressed frequently as an efficient way of preserving the whole temporal information of sequence with a considerably smaller amount of data. Such compact video representation is suitable for the purpose of video browsing in limited storage or transmission bandwidth environments. In a case like this, the controllability of the total key frame number (i.e. key frame rate) depending on the storage or bandwidth capacity is an important requirement of a key frame selection method. In this paper, we present a sequential key frame selection method when the number of key frames is given as a constraint. It first selects the desired number of initial key frames and determines non-overlapping initial time intervals that are represented by each key frame. Then, it adjusts the positions of key frames and time intervals by iteration, which minimizes the distortion. Experimental result demonstrates the improved performance of our algorithm over the existing approaches.

• Journal of Broadcast Engineering
• /
• v.2 no.2
• /
• pp.86-93
• /
• 1997

Fast growth of multimedia applications requires new functions for video data processing. such as obj;cted-based video representation and manipulation. which are not supported by 11PEG-l and 11PEG-2. To support these requirements. 11PEG-4 video coding allows users to manipulate every video object easily by decomposing a scene into several video objects and coding each of them independently. However. the large amount of computations and flexible structure of 11PEG-4 video CODEC make it difficult to be implemented by either the general purpose DSP or a dedicated VLSI. In this paper, we propose a hardware architecture using a hybrid of a high performance programmable DSP and an application specific IC to implement a flexible 11PEG-4 video codec requiring the large amount of computations. The application specific IC has the functions of motion estimation and compensation.

• The Journal of the Korea Contents Association
• /
• v.13 no.6
• /
• pp.77-85
• /
• 2013

This study aims to analyze the effects that video image in Seo-an's , claiming to China's first gigantic historic dance drama, has on the performance; it focuses on investigating which video image is used to accomplish the effects in showing specific themes and materials in . Image is meant by 'reflection of object', such as movie, television, dictionary, etc, with its coverage being extensive. The root of a word, image', is founded on imitary, signifying specifically and mentally visual representation. In other words, video image is considered combination of two synonymous words, 'video' and 'image'. Video is not just comprehension of traditional art genre, like literary value, theatrical qualities, and artistry of scenario, but wholeness as product, integrating original functions of all kinds of art and connecting subtle image creation of human being. The effects of video image represented in are as followings; first, expressive effect of the connotative meaning, reflecting the spirit of the age and its culture. Second, imaginary identification. Third, transformation scene. Fourth, dramatic interest through immersion. Last but not least, visual effect by dint of dimension of performance.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.5
• /
• pp.32-37
• /
• 2008

To ensure access to rapidly growing video collection, video indexing is becoming more and more important. In this paper, video ontology system for retrieving a video data based on a scene unit is proposed. The proposed system creates a semantic scene as a basic unit of video retrieval, and limits a domain of retrieval through a subject of that scene. The content of semantic scene is defined using the relationship between object and event included in the key frame of shots. The semantic gap between the low level feature and the high level feature is solved through the scene ontology to ensure the semantic-based retrieval.

• ETRI Journal
• /
• v.29 no.3
• /
• pp.353-362
• /
• 2007

In this paper, we propose a novel unsupervised video object extraction algorithm for individual images or image sequences with low depth of field (DOF). Low DOF is a popular photographic technique which enables the representation of the photographer's intention by giving a clear focus only on an object of interest (OOI). We first describe a fast and efficient scheme for extracting OOIs from individual low-DOF images and then extend it to deal with image sequences with low DOF in the next part. The basic algorithm unfolds into three modules. In the first module, a higher-order statistics map, which represents the spatial distribution of the high-frequency components, is obtained from an input low-DOF image. The second module locates the block-based OOI for further processing. Using the block-based OOI, the final OOI is obtained with pixel-level accuracy. We also present an algorithm to extend the extraction scheme to image sequences with low DOF. The proposed system does not require any user assistance to determine the initial OOI. This is possible due to the use of low-DOF images. The experimental results indicate that the proposed algorithm can serve as an effective tool for applications, such as 2D to 3D and photo-realistic video scene generation.

• Journal of Broadcast Engineering
• /
• v.18 no.5
• /
• pp.680-690
• /
• 2013

In this paper, we propose a real-time temporal synchronization and compensation algorithm in stereoscopic video. Many temporal asynchronies are caused in the video editing stage and due to different transmission delays. These temporal asynchronies can degrade the perceived 3D quality. The goal of temporal alignment is to detect and to measure the temporal asynchrony and recover synchronization of the two video streams. In order to recover synchronization of the two video streams, we developed a method to detect asynchronies between the left and the right video streams based on a novel spatiogram information, which is a richer representation, capturing not only the values of the pixels but their spatial relationships as well. The proposed novel spatiogram additionally includes the changes of the spatial color distribution. Furthermore, we propose a block-based method for detection of the pair frame instead of one frame-based method. Various 3D experiments demonstrate the effectiveness of the proposed method.

• Journal of the Korean Chemical Society
• /
• v.54 no.4
• /
• pp.465-470
• /
• 2010

Using interesting video clips corresponding to lesson subjects for students who favour visual representation is one of the good methods to enhance students' preference for science class. There are many moving picture web sites to get video clips easily via internet and 'YouTube' is very popular and one of the largest reservoir. In this study, every student in the 'Chemistry I' class, which is a class for 11th grade, was requested to search a video clip corresponding to lesson subjects and to make a presentation in the class. After 1st semester, students' response about the class using YouTube was examined by survey. As a result, students preferred and were interested in the class using YouTube than class centered on textbook. And students preferred YouTube clips showing unusual experiments that were related with contents of subject. In addition, experiments and watching their real phenomena were an interesting factor and helpful factor of learning chemistry in YouTube video clips, respectively. However, translation of English used in the video clips seemed to be a difficult part for students.

• Journal of Information Processing Systems
• /
• v.16 no.1
• /
• pp.6-29
• /
• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.