• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.1-4
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• 2007

In this paper, we propose an efficient scheme for automatically detecting highlight scenes in soccer videos. Highlights are defined as shooting scenes and goal scenes. Through the analysis of soccer videos, we notice that most of highlight scenes are shown around the goal post area. It is also noticed that the TV camera zooms in a setter player or spectators after the highlight stones. Detection of highlight scenes for soccer videos consists of three steps. The first step is the extraction of the playing field using a statistical threshold. The second step is the detection of goal posts. In the final step, we detect a zooming of a soccer player or spectators by using connected component labeling of non-playing field. In order to evaluate the performance of our method, the precision and the recall are computed. Experimental results have shown the effectiveness of the proposed method, with 95.2% precision and 85.4% recall.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.409-418
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• 2022

In this paper, we propose SAAnnot-C3Pap, a semi-automatic annotation method for obtaining ground truth of a player's posture. In order to obtain ground truth about the two-dimensional joint position in the existing music domain, openpose, a two-dimensional posture estimation method, was used or manually labeled. However, automatic annotation methods such as the existing openpose have the disadvantages of showing inaccurate results even though they are fast. Therefore, this paper proposes SAAnnot-C3Pap, a semi-automated annotation method that is a compromise between the two. The proposed approach consists of three main steps: extracting postures using openpose, correcting the parts with errors among the extracted parts using supervisely, and then analyzing the results of openpose and supervisely. Perform the synchronization process. Through the proposed method, it was possible to correct the incorrect 2D joint position detection result that occurred in the openpose, solve the problem of detecting two or more people, and obtain the ground truth in the playing posture. In the experiment, we compare and analyze the results of the semi-automated annotation method openpose and the SAAnnot-C3Pap proposed in this paper. As a result of comparison, the proposed method showed improvement of posture information incorrectly collected through openpose.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.607-609
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• 2003

동영상 자동 분석은 비디오 데이터의 내용 기반 색인과 검색을 위한 첫 단계이다. 본 논문에서는 정형적인 구조를 가진 뉴스와는 달리 비정형적인 특성을 가진 축구 동영상에서 사용자의 관심이 되는 하이라이트 장면의 영상 특징을 이용하여 그 구조를 분석하여 하이라이트 장면을 검출하는 방법을 제안한다. 이전 연구를 토대로 그라운드영역과 골대 유무에 따라 하이라이트 후보 장면을 찾는 과정에서 경기마다 달라지는 임계값에 영향을 받지 않는 알고리즘을 제안하였다. 실험결과 제안된 방법이 여러 종류의 축구 경기 하이라이트 분석에 있어서 그 성능이 우수함을 확인할 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.11
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• pp.69-77
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• 2004

An efficient board-level interconnect test algorithm is proposed considering both the ground bounce effect and the delay fault detection. The proposed algorithm is capable of IEEE 1149.1 interconnect test, negative ground bounce effect prevention, and also detects delay faults as well. The number of final test pattern set is not much different with the previous method, even our method enables to detect the delay faults in addition to the abilities the previous method guarantees.

• Journal of Broadcast Engineering
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• v.11 no.2 s.31
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• pp.207-221
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• 2006

A fully automatic and computationally efficient method is proposed for intelligent display of soccer video on small multimedia mobile devices. The rapid progress of the multimedia signal processing has contributed to the extensive use of multimedia devices with a small LCD panel. With these emerging small mobile devices, the video sequences captured for standard- or HDTV broadcasting may give the small-display-viewers uncomfortable experiences in understanding what is happening in a scene. For instance, in a soccer video sequence taken by a long-shot camera technique, the tiny objects (e.g., soccer ball and players) may not be clearly viewed on the small LCD panel. Thus, an intelligent display technique is needed for small-display-viewers. To this end, one of the key technologies is to determine region of interest (ROI), which is a part of the scene that viewers pay more attention to than other regions. In this paper, the focus is on soccer video display for mobile devices. Instead of taking visual saliency into account, we take domain-specific approach to exploit the characteristics of the soccer video. The proposed scheme includes three modules; ground color learning, shot classification, and ROI determination. The experimental results show the propose scheme is capable of intelligent video display on mobile devices.