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Abstract: It is becoming increasingly clear that it is humanly impossible to browse, navigate, and search a deluge of data from cameras and other sensors in a variety of applications including retail, public safety, transportation, healthcare, and education. The practical systems that we built, although in pursuit of different business objectives, share a common goal, which is to intelligently and efficiently analyze and extract salient and “interesting” information from an overwhelming amount of data, while being able to effectively ignore large portions of the corpus which is uneventful or uninteresting to the user. I will summarize a variety of computer vision, machine learning and system optimization techniques that we used to successfully address different technical and business challenges, and to deliver differentiating performance to meet our customers' expectations. 

Contributors: Lisa Brown, Jonathan Connell, Ankur Datta, Quanfu Fan, Rogerio Feris, Norman Haas, Jun Li, , Ying Li, : Sachiko Miyazawa, Juan Moreno, Hoang Trinh, Michele Merler, Quoc-Bao Nguyen, John Smith, Liangliang Cao, Noel Codella 

Speaker Bio: Sharath Pankanti is at Research Staff Member and Manager with Exploratory Computer Vision Group at IBM T J Watson Research Center. He received a B.S. degree in Electrical and Electronics Engg from College of Engineering Pune in 1984, M.Tech in Computer Science from Hyderabad Central University in 1988 and Ph.D. degree in Computer Science from the Michigan State University in 1995. He has published over 100 peer-reviewed publications and has contributed to over 50 inventions related to biometrics, privacy, object detection, and recognition. Dr. Pankanti is an IEEE Fellow. His experience spans a number of safety, productivity, and security focused projects involving