Although computer vision has still rather low acceptance as a reliable tool for automated analysis of general human behavior it can very useful for automated monitoring of relatively structured processes on a 24/7 basis even under adverse conditions. This presentation deals with the case study of human behavior analysis on an automobile production line, where noise and occlusions pose serious challenges to the tracking algorithms. We examine issues such as feature extraction, modeling of noisy time series, multi-camera fusion, extraction of events in real time and active learning in time series using these challenging videos.
Part of this work has been conducted in the framework of the SCOVIS project (www.scovis.eu).
Dr Dimitrios Kosmopoulos received B. Eng. degree in Electrical and Computer Engineering from the National Technical University of Athens in 1997 and the PhD degree from the same institution in 2002. He is currently an assistant professor at TEI Crete. Previously he was at Rutgers University, CBIM Lab and at the University of Texas at Arlignton, Department of Computer Science and Engineering. He has been a research scientist in the Computational Intelligence Laboratory of the Institute of Informatics and Telecommunications in the NCSR Demokritos - Greece. His research focuses on learning methods for video processing and time series analysis. He has taught courses in algorithms, image processing, robotics, pattern recognition and AI.
