• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.28-38
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• 2001

Traffic information can be broadly categorized into point information and spatial information. Point information can be obtained by chocking only the presence of vehicles at prespecified points(small area), whereas spatial information can be obtained by monitoring large area of traffic scene. To obtain spatial information by image processing, we need to track vehicles in the whole area of traffic scene. Image detector system based on global tracking consists of video input, vehicle detection, vehicle tracking, and traffic information measurement. For video input, conventional approaches used auto iris which is very poor in adaptation for sudden brightness change. Conventional methods for background generation do not yield good results in intersections with heave traffic and most of the early studies measure only point information. In this paper, we propose user-controlled iris method to remedy the deficiency of auto iris and design flame difference-based background generation method which performs far better in complicated intersections. We also propose measurement method for spatial traffic information such as interval volume/lime/velocity, queue length, and turning/forward traffic flow. We obtain measurement accuracy of 95%∼100% when applying above mentioned new methods.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.701-708
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• 2004

Existing contents-based video retrieval systems search by using a single method such as annotation-based or feature-based retrieval. Hence, it not only shows low search efficiency, but also requires many efforts to provide system administrator or annotator with a perfect automatic processing. Tn this paper, we propose an agent-based, and automatic and unified semantics-based video retrieval system, which support various semantics-retrieval of the massive video data by integrating the feature-based retrieval and the annotation-based retrieval. The indexing agent embodies the semantics about annotation of extracted key frames by analyzing a fundamental query of a user and by selecting a key-frame image that is ed by a query. Also, a key frame selected by user takes a query image of the feature-based retrieval and the indexing agent searches and displays the most similar key-frame images after comparing query images with key frames in the database by using the color-multiple-partition histogram techniques. Furthermore, it is shown that the performance of the proposed system can be significantly improved.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.298-306
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• 2013

The scale invariant feature transform (SIFT) is an effective algorithm used in object recognition, panorama stitching, and image matching. However, due to its complexity, real-time processing is difficult to achieve with current software approaches. The increasing availability of parallel computers makes parallelizing these tasks an attractive approach. This paper proposes a novel parallel approach for SIFT algorithm implementation using a block filtering technique in a Gaussian convolution process on the SIMD Pixel Processor. This implementation fully exposes the available parallelism of the SIFT algorithm process and exploits the processing and input/output capabilities of the processor, which results in a system that can perform real-time image and video compression. We apply this implementation to images and measure the effectiveness of such an approach. Experimental simulation results indicate that the proposed method is capable of real-time applications, and the result of our parallel approach is outstanding in terms of the processing performance.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.76-85
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• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.953-958
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• 2007

Passenger safety is a primary concern of railway system but, it has been urgent issue that dozens of people are killed every year when they are fallen from train platforms. In this paper, we propose a vision based monitoring system for railway station platform. The system immediately perceives dangerous factors of passengers on the platform by using image processing technology. To monitor almost entire length of the track line in the platform, we use several video cameras. Each camera conducts surveillance its own preset monitoring area whether human or dangerous object was fallen in the area. Moreover, to deal with the accident immediately, the system provides local station, central control room employees and train driver with the video information about the accident situation including alarm message. This paper introduces the system overview and detection process with experimental results. According to the results, we expect the proposed system will play a key role for establishing highly intelligent monitoring system in railway.

• Journal of Broadcast Engineering
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• v.9 no.2
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• pp.131-141
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• 2004

Current binocular stereoscopic displays cause visual discomfort when objects with large disparities are present in the scene. One solution for improving visual comfort is synthetic depth-of-field processing, which simulates the characteristics of a human visual system. With this technique, visual comfort is improved by blurring portions of the background and/or foreground in the scene. However, this technique has the drawback of degrading overall image quality because the blurring is typically applied to both left and right images. To alleviate the visual discomfort, we propose a novel disparity-based asymmetrical filtering technique. Proposed technique applies the filtering to the image of one eye only, and controls the blur level according to the disparity information between stereoscopic images. We investigate the effects of this technique on stereoscopic video by measuring visual comfort and apparent sharpness. Our results indicate that disparity-based asymmetrical filtering can improve visual comfort of stereoscopic video while it maintains apparent sharpness if unfixated regions with large disparities are blurred under the appropriate filtering condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

• Journal of Korea Spatial Information System Society
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• v.10 no.2
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• pp.13-24
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• 2008

A next generation video based car navigation is researched to supplement the drawbacks of existed 2D based navigation and to provide the various services for safety driving. The components of this navigation system could be a load object database, identification module for load lines, and crossroad identification module, etc. In this paper, we proposed the traffic lights and road sign recognition method which can be effectively exploited for crossroad recognition in video-based car navigation systems. The method uses object color information and other spatial features in the video image. The results show average 90% recognition rate from 30m to 60m distance for traffic lights and 97% at 40-90m distance for load sign. The algorithm also achieves 46msec/frame processing time which also indicates the appropriateness of the algorithm in real-time processing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1708-1714
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• 2010

In this paper, the full color LED billboard or a general quality improvement methods of quality gamma correction, brightness, and brightness adjustment, etc., regardless of the overall color of images uniformly bright or dark have been taken care of. The video itself, but simply expressed as a uniform brightness of a certain size, how to adjust the brightness of input video signal does not reflect the characteristics of the entire screen with just a lighter or darker line is only feeling was brought. So, unlike conventional video transmission system with new LED display technology in the histogram analysis of image data is input by the input image data by determining the luminance values of the attributes are reflected, as appropriate based on the histogram of the distribution of brightness values By controlling the LED display is expressed in the uniform image can improve the brightness control, histogram distribution of the image as full color billboards driven processing technology is proposed.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.384-389
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• 2008

Recently, the necessity of systems which effectively store and retrieve video data has increased. Conventional video retrieval systems retrieve data using menus or text based keywords. Due to the lack of information, many video clips are simultaneously searched, and the user must have a certain level of knowledge to utilize the system. In this paper, we suggest a natural language based conversational video retrieval system that reflects users' intentions and includes more information than keyword based queries. This system can also retrieve from events or people to their movements. First, an event database is constructed based on meta-data which are generated by domain analysis for collected video in an office environment. Then, a script database is also constructed based on the query pre-processing and analysis. From that, a method to retrieve a video through a matching technique between natural language queries and answers is suggested and validated through performance and process evaluation for 10 users The natural language based retrieval system has shown its better efficiency in performance and user satisfaction than the menu based retrieval system.