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Other Workshops

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Title [seminar] December 16th, 2015 <Prof. Yung-Hsiang Lu, Purdue University> Date 2015.11.24 15:21


KAIST 장래혁 교수 연구실에서 아래와 같이 세미나를 개최하오니 많은 관심과 참여를 부탁 드립니다.


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□ 일 시 : 2015 12 16() 16:00 ~

□ 장 소 : KAIST 김병호 IT 융합빌딩 (IT Convergence Center(N1)) 1층 다목적홀 (117)

□ 연 사 : Prof. Yung-Hsiang Lu (Purdue University)

□ 주 제 : Opportunities and Challenges in Global Network Cameras

Abstract :

Millions of network cameras have been deployed. Many of these cameras provide publicly available data, continuously streaming live views of national parks, city halls, streets, highways, and shopping malls. A person may see multiple tourist attractions through these cameras, without leaving home. Researchers may observe the weather in different cities. Using the data, it is possible to observe natural disasters at a safe distance. News reporters may obtain instant views of an unfolding event. A spectator may watch a celebration parade from multiple locations using street cameras. Despite the many promising applications, the opportunities of using global network cameras for creating multimedia content have not been fully exploited. The opportunities also bring forth many challenges. Managing the large amount of data would require fundamentally new thinking. The data from network cameras are unstructured and have few metadata describing the content. Searching the relevant content would be a challenge. Because network cameras continuously produce data, processing must be able to handle the streaming data. This imposes stringent requirements of the performance.

In this presentation, I will share the experience building a software system that aims to explore the opportunities using the data from global network cameras. CAM2 (Continuous Analysis of Many CAMeras, is cloud-based system for studying the worldwide phenomena using network cameras. CAM2 provides an event-based API (application programming interface) and is open to researchers to analyze the data for their studies. The web interface allows users to select the cameras for analysis. The cloud computing engine can scale in response to the needs of analysis programs.


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