• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1050-1060
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• 2011

With increasing need for indexing of persons in a large video database, automatic indexing has been attracting great interest which takes advantage of automatic tagging instead of the time-consuming and costly manual tagging. However, automatic indexing approach should provide a degree of recognition proximity because it cannot identify the persons with accuracy of 100%. In this paper, we propose an efficient storage method for storing posting lists efficiently and a novel ranking technique of ordering relevant videos for efficient retrieval. Through experiment evaluations we have shown that our storage method exhibits good performance in compressing the posting list. We have also shown that the proposed ranking method is effective for finding relevant videos.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.519-526
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• 2022

Identifying and searching for characters appearing in scenes during multimedia video editing is an arduous and time-consuming process. Applying artificial intelligence to labor-intensive media editing tasks can greatly reduce media production time, improving the creative process efficiency. In this paper, a method is proposed which combines existing artificial intelligence based techniques to automate character recognition and search tasks for video editing. Object detection, face detection, and pose estimation are used for character localization and face recognition and color space analysis are used to extract unique representation information.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.85-91
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• 2020

Recently, video-taping equipment such as CCTV have been seeing more use for crime prevention and general safety concerns. Since these video-taping equipment operates all throughout the day, the need for security personnel is lessened, and naturally costs incurred from managing such manpower should also decrease. However, technology currently used predominantly lacks self-sufficiency when given the task of searching for a specific object in the recorded video such as a person, and has to be done manually; current security-based video equipment is insufficient in an environment where real-time information retrieval is required. In this paper, we propose a technology that uses the latest deep-learning technology and OpenCV library to quickly search for a specific person in a video; the search is based on the clothing information that is inputted by the user and transmits the result in real time. We implemented our system to automatically recognize specific human objects in real time by using the YOLO library, whilst deep learning technology is used to classify human clothes into top/bottom clothes. Colors are also detected through the OpenCV library which are then all combined to identify the requested object. The system presented in this paper not only accurately and quickly recognizes a person object with a specific clothing, but also has a potential extensibility that can be used for other types of object recognition in a video surveillance system for various purposes.