Readings in Vision and Graphics
edited by Luc Van Gool, Gábor Székely, Markus Gross, Bernt Schiele
Daniel Eugen Roth,
Real-Time Multi-Object Tracking
2010. XIV, 108 pages. EUR 64,00. ISBN-10: 3-86628-308-3
New video cameras are installed in growing numbers in private and public places, producing a huge amount of image data. The need to process and analyze the data automatically in real-time is critical for applications such as visual surveillance. Of particular interest is the tracking of moving objects such as pedestrians and cars.
This book presents two real-time visual object tracking methods, multiple prototype systems and an event-based performance evaluation metric. Both proposed monocular object trackers use a modular tracking framework based on Bayesian per-pixel classification. It segments an image into foreground and background objects based on observations of object appearances and motions. Both systems adapt to changing lighting conditions, handle occlusions, and work in real-time. Multiple prototype systems are presented for privacy applications in video surveillance and cognitive multi-resolution behavior analysis.
Furthermore, a performance evaluation method is presented and applied to different state-of-the-art trackers based on the successful detection of semantic high level events. The high level events are extracted automatically from the different trackers and their varying types of low level tracking results. The general new event metric is used to compare our tracking method and the other tracking methods against ground truth of multiple public datasets.
About the Author:
Daniel Roth received his PhD degree (Dr. sc. ETH) in 2009 and his MSc ETH in Electrical Engineering and Information Technology (Dipl. El.-Ing. ETH) in 2004, both from ETH Zurich. From 2004 to 2009, he was research assistant at the Computer Vision Laboratory of ETH Zurich, with Prof. Luc Van Gool. His research interests include multi-object tracking, performance evaluation and real-time systems.
Keywords: multi-object tracking, event detection, performance evaluation, real-time, visual surveillance, computer vision
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