• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.151-154
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• 2000

We propose a statically motivated scene change detection algorithm. As the difference between the neighboring frames will generate peaks at scene boundaries, the problem of detecting fast scene changes is equivalent to detecting peaks in a given sequence. In this paper, the peak detection is performed via several statistics, namely the sample means and variances. For eliminating flash lights as well as detecting fast scene changes within a small number of frames, we have opted to use a two-stage process for computing the necessary statistics. The results indicate superiority of necessary statistics. The results indicate superiority of the proposed algorithm over the previously reported algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.250-251
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• 2013

In this paper, we would like to extract criminals doing this criminal offense to smoke in elevators. Extraction method detect difference value using modified color-$X^2$-test and it was normalized. Next, we find frames that has occurred scene change in successive frames using the four-step algorithm of scene change detection. Finally, we present the method of smoking image retrieval and extraction in stored large amount of video.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.860-868
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• 2008

Human detection techniques in outdoor scenes have been studied for a long time to watch suspicious movements or to keep someone from danger. However there are few methods of human detection in overhead or near-field view scenes, while lots of human detection methods in far-field view scenes have been developed. In this paper, a set of five features useful for human detection in overhead view scenes and another set of four useful features in near-field view scenes are suggested. Eight feature-candidates are first extracted by analyzing geometrically varying characteristics of moving objects in samples of video sequences. Then highly contributed features for each view scene to classifying human from other moving objects are selected among them by using a neural network learning technique. Through experiments with hundreds of moving objects, we found that each set of features is very useful for human detection and classification accuracy for overhead view and near-field view scenes was over 90%. The suggested sets of features can be used effectively in a PTZ camera based surveillance system where both the overhead and near-field view scenes appear.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.778-786
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• 2002

Scene boundary detection is important in the understanding of semantic structure from video data. However, it is more difficult than shot change detection because scene boundary detection needs to understand semantics in video data well. In this paper, we propose a new approach to scene segmentation using contextual information in video data. The contextual information is divided into two categories: local and global contextual information. The local contextual information refers to the foreground regions' information, background and shot activity. The global contextual information refers to the video shot's environment or its relationship with other video shots. Coherence, interaction and the tempo of video shots are computed as global contextual information. Using the proposed contextual information, we detect scene boundaries. Our proposed approach consists of three consecutive steps: linking, verification, and adjusting. We experimented the proposed approach using TV dramas and movies. The detection accuracy of correct scene boundaries is over than 80%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.357-358
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• 2021

Although many people watch the scene with multiple surveillance cameras, it is difficult to ensure that immediate action can be taken in the event of a crime. Therefore, there is a need for a "crime behavior detection system" that can analyze images in real time from multiple surveillance cameras installed in elevators, call immediate crime alerts, and track crime scenes and times effectively. In this paper, a study was conducted to detect violent scenes occurring in elevators using Scene Change Detection. For effective detection, an x2-color histogram combining color histogram and histogram was applied.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.115-122
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• 2001

A scene change detection is an important step for video indexing and retrieval. This paper proposes an algorithm by a phased algorithm for fast and accurate detection of abrupt scene changes in an MPEG compressed domain with minimal decoding requirements and computational effort. The proposed method compares two successive I-frames for locating a scene change occurring within the GOP and uses macroblock-coded type information contained in B-frames to detect the exact frame where the scene change occurred. The algorithm has the advantage of speed, simplicity and accuracy. In addition, it requires less amount of storage. The experiment results demonstrate that the proposed algorithm has better detection performance, such as precision and recall rate, than the existing method using all DC images.

• ETRI Journal
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• v.18 no.2
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• pp.61-74
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• 1996

We propose two new rate control schemes to improve MPEG-2 rate control in view of visual quality when scene changes happen. Two proposed schemes are characterized by real-time and non real-time improvement to reduce the impact of scene changes. We also propose a new target-bit prediction method using spatial activity of pictures and present a simple and efficient scene change detection scheme using signed difference of mean absolute difference (MAD). Computer simulation results show that the proposed real-time algorithm effectively alleviates visual quality degradation after scene changes. The proposed non real-time algorithm gives maximum 2 dB improvement in peak signal-to-noise ratio (PSNR) at a scene-changed picture, compared with MPEG-2 rate control scheme and it shows better quality than the real-time one.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2S
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• pp.629-637
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• 2000

The content-based information retrieval for a multimedia database uses feature information extracted from the compressed videos. This paper presents an effective method to detect scene changes from compressed videos. Scene changes are detected with DC values of DCT coefficients in MPEG-1 encoded video sequences. Instead of decoding full frames. partial macroblocks of each frame, horizontal and vertical macroblocks, are decoded to detect scene changes. This method detects abrupt scene changes by decoding minimal number of blocks and saves a lot of computation time. The performance of the proposed algorithm is analyzed based on the precision and the recall. The experimental results show the effectiveness in computation time and detection rate to detect scene changes of various MPEG-1 video streams.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.329-336
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• 2023

This paper introduces an edge AI-based scene-specific object detection system for long-term traffic management, focusing on analyzing congestion and movement via cameras. It aims to balance fast processing and accuracy in traffic flow data analysis using edge computing. We adapt the YOLOv5 model, with four heads, to a scene-specific model that utilizes the fixed camera's scene-specific properties. This model selectively detects objects based on scale by blocking nodes, ensuring only objects of certain sizes are identified. A decision module then selects the most suitable object detector for each scene, enhancing inference speed without significant accuracy loss, as demonstrated in our experiments.

• Journal of the Korea Society of Computer and Information
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• v.4 no.3
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• pp.28-34
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• 1999

In this paper. we propose the method of Scene Change Detection for video sequence using the DC components and the moving vectors in the Macro Blocks in the DCT blocks. The proposed method detects the Scene Change which would not be related with the specific sequences in the compressed MPEG domain. To do this. we define new metrics for Scene Change Detection using the features of picture component and detect the exact Scene Change point of B-pictures using the characteristics of B-picture's sharp response for the moving vectors. In brief, we will detect the cut point using I-picture and the gradual scene changes such as dissolve, fade, wipe, etc. As a results, our proposed method shows good test results for the various MPEG sequences.