• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.219-222
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• 2001

Moving objects in video data are main elements for video analysis and retrieval. In this paper, we propose a new algorithm for tracking and segmenting moving objects in color image sequences that include complex camera motion such as zoom, pan and rotating. The Proposed algorithm is based on the Mean-shift color segmentation and stochastic region matching method. For segmenting moving objects, each sequence is divided into a set of similar color regions using Mean-shift color segmentation algorithm. Each segmented region is matched to the corresponding region in the subsequent frame. The motion vector of each matched region is then estimated and these motion vectors are summed to estimate global motion. Once motion vectors are estimated for all frame of video sequences, independently moving regions can be segmented by comparing their trajectories with that of global motion. Finally, segmented regions are merged into the independently moving object by comparing the similarities of trajectories, positions and emerging period. The experimental results show that the proposed algorithm is capable of segmenting independently moving objects in the video sequences including complex camera motion.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.110-114
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• 2015

This paper reports a novel object-detection technique in video sequences. The proposed algorithm consists of detection of objects in illuminated and occluded videos by using object features and a neural network technique. It consists of two functional modules: region-based object feature extraction and continuous detection of objects in video sequences with region features. This scheme is proposed as an enhancement of the Lowe's scale-invariant feature transform (SIFT) object detection method. This technique solved the high computation time problem of feature generation in the SIFT method. The improvement is achieved by region-based feature classification in the objects to be detected; optimal neural network-based feature reduction is presented in order to reduce the object region feature dataset with winner pixel estimation between the video frames of the video sequence. Simulation results show that the proposed scheme achieves better overall performance than other object detection techniques, and region-based feature detection is faster in comparison to other recent techniques.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.867-870
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• 1998

Video data is usually stored in a compressed format in order to reduce the storage space. For efficient browsing, searching, and retrieval of compressed video sequences, size-reduced images (or DC images which are formed with block DC coefficients) are generally preferred to avoid unnecessary computational complexity. In this paper, we propose a DC image extraction scheme appropriate for scene analysis and efficient browsing of compressed video sequences. The proposed algorithm utilizes predicted low frequency AC coefficients to achieve better approximation and to reduce the error drift. Due to the AC prediction based on a quadratic surface model, the proposed scheme requires no additional memory compared with the previous zero-order or first-order approximation scheme. Simulation results show that the proposed scheme achieves better subjective and objective quality with minor additional operations.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.341-348
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• 2022

Formulaic sequences are continuous or discontinuous series of words that are seemingly treated like single units. Formulaic sequences play a key role in language development, and formulaic sequences acquisition determines the success or failure of language development. This study proposes a reading aloud activity as a way for international students to learn formulaic sequences. A class focused on reading aloud was conducted with 41 international students taking a general English course at a university in Seoul. For 15 weeks, video lectures and real-time Zoom classes were conducted in parallel. The animated film Frozen was used as course material. In the video lectures, the teacher interpreted the movie script in easy Korean and read aloud formulaic sequences. Students were tasked with reading the sentences with formulaic sequences aloud, recording themselves reading aloud, and submitting their recordings. During real-time class meetings, students performed the activity of reading aloud the formulaic sequences they had studied in the video lectures. There was a significant increase in the interpretation and sentence writing of formulaic sequences in participants' post-evaluation compared to the pre-evaluation. Through the study's survey, students exhibited positive views in the affective domains.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.4 s.31
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• pp.69-74
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• 2004

By the expanding of infrastructure caused by urbanization, new technologies are required to manage various kinds of facilities. GIS has been appraised as valuable technology for facilities management since the 1990s. Therefore, the long and mid term GIS construction plan has been established by the national government and the local government. Some facilities management systems have been built and developed for suppling user-friendly functions. From this point of view, the information system based on the video sequences is considered a more effective way to improve the defects of conventional GIS using the digital map or the image as the base map. Using the video sequences as a base map, the availability of the system ill be increased because the real world information can be furnished to the users. In this study, through the connection between the GIS data, the digital map and the attribute data, and the video sequences taken from the airship using the video geo-referencing and the object tracking, we developed the facilities management system as a prototype which can effectively manage the road utilities. We also presented potentialities of the suggested system for facility management based on the video sequences.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.132-140
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• 2011

Ultra-High Definition Television (U-HDTV) is a promising candidate for the next generation television. Since the U-HDTV video signal requires a huge amount of data, parallel implementation of the U-HDTV compression system is highly demanding. In the conventional parallel video codec, a video is divided into sub-sequences and the sub-sequences are independently encoded. In this paper, for efficient parallel processing, we propose a pipelined encoding structure which exploits cross-correlation among the sub-sequences. The experimental results demonstrate that the proposed technique improves the coding efficiency and provides the sub-sequences of the balanced visual quality.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.45-52
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• 2004

According tothe development of digital media technologies various algorithms for video sequence matching have been proposed to match the video sequences efficiently. A large number of video sequence matching methods have focused on frame-wise query, whereas a relatively few algorithms have been presented for video sequence matching or video shot matching. In this paper, we propose an efficientalgorithm to index the video sequences and to retrieve the sequences for video sequence query. To improve the accuracy and performance of video sequence matching, we employ the Cauchy function as a similarity measure between histograms of consecutive frames, which yields a high performance compared with conventional measures. The key frames extracted from segmented video shots can be used not only for video shot clustering but also for video sequence matching or browsing, where the key frame is defined by the frame that is significantly different from the previous fames. Several key frame extraction algorithms have been proposed, in which similar methods used for shot boundary detection were employed with proper similarity measures. In this paper, we propose the efficient algorithm to extract key frames using the cumulative Cauchy function measure and. compare its performance with that of conventional algorithms. Video sequence matching can be performed by evaluating the similarity between data sets of key frames. To improve the matching efficiency with the set of extracted key frames we employ the Cauchy function and the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed method yields the high matching performance and accuracy with a low computational load compared with conventional algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.69-72
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• 2011

In this paper, we propose a method to track the movement of camera from the video sequences. This method is useful for video analysis and can be applied as pre-processing step in some application such as video stabilizer and marker-less augmented reality. First, we extract the features in each frame using corner point detection. The features in current frame are then compared with the features in the adjacent frames to calculate the optical flow which represents the relative movement of the camera. The optical flow is then analyzed to obtain camera movement parameter. The final step is camera movement estimation and correction to increase the accuracy. The method performance is verified by generating a 3D map of camera movement and embedding 3D object to the video. The demonstrated examples in this paper show that this method has a high accuracy and rarely produce any jitter.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.347-356
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• 2006

A video stream can be represented by a sequence of data points in a multidimensional space. In this paper, we introduce a trend vector that approximates values of data points in a sequence and represents the moving trend of points in the sequence, and present a pattern similarity matching method for data sequences using the trend vector. A sequence is partitioned into multiple segments, each of which is represented by a trend vector. The query processing is based on the comparison of these vectors instead of scanning data elements of entire sequences. Using the trend vector, our method is designed to filter out irrelevant sequences from a database and to find similar sequences with respect to a query. We have performed an extensive experiment on synthetic sequences as well as video streams. Experimental results show that the precision of our method is up to 2.1 times higher and the processing time is up to 45% reduced, compared with an existing method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5856-5864
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• 2011

In this paper, we propose a moving object detection method in road video sequences acquired from a stationary camera. Our proposed method is based on the background subtraction method using Gaussian pyramids in both the background images and input video frames. It is more effective than pixel based subtraction approaches to reduce false detections which come from the mis-registration between current frames and the background image. And to determine a threshold value automatically in subtracted images, we calculate the threshold value using Otsu's method in each frame and then apply a scalar Kalman filtering to the threshold value. Experimental results show that the proposed method effectively detects moving objects in road video images.