Research Collection. Real-time multi-object tracking. Doctoral Thesis. ETH Library. Author(s): Roth, Daniel Eugen. Publication Date: 2010
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1 Research Collection Doctoral Thesis Real-time multi-object tracking Author(s): Roth, Daniel Eugen Publication Date: 2010 Permanent Link: Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library
2 Diss. ETH No Real-Time Multi-Object Tracking A dissertation submitted to the SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH for the degree of Doctor of Sciences ETH presented by DANIEL EUGEN ROTH MSc ETH in Electrical Engineering and Information Technology born 28 th January 1978 citizen of Zollikon (ZH) and Hemberg (SG) accepted on the recommendation of Prof. Dr. Luc Van Gool, ETH Zurich and K.U. Leuven, examiner Prof. Dr. Thomas B. Moeslund, Aalborg University, co-examiner 2009
3 Abstract 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 or live sports analysis. Of particular interest is the tracking of moving objects such as pedestrians and cars. This work presents two visual object tracking methods, multiple prototype systems and an event-based performance evaluation metric. The first object tracker uses a flexible 2D environment modeling to track arbitrary objects. The second method detects multiple object classes using a camera calibration, therefore operates in 2.5D and performs more robustly in crowded situations. 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.
4 Zusammenfassung Die steigende Zahl von installierten Kameras an öffentlichen und privaten Orten führt zu einer stetig wachsenden Bilderflut, die nicht oder nur eingeschränkt analysiert werden kann. Die automatische Auswertung und Analyse dieser Videos in Echtzeit ist daher Voraussetzung für Anwendungen der Videoüberwachung oder zur live Analyse von Sportarten. Speziell interessant ist hierbei die Verfolgung von sich bewegenden Objekten, wie zum Beispiel Personen oder Autos. Diese Dissertation beschreibt zwei Methoden sowie mehrere Prototypsysteme zur automatischen Detektierung und Verfolgung von Objekten. Zudem wird eine Bewertungs- und Vergleichsmethode für solche Systeme präsentiert, die das Erkennen von einzelnen Ereignissen durch die Systeme auswertet. Die erste der beiden präsentierten Objektverfolgungsmethoden arbeitet rein zweidimensional (2D) mit beliebigen Objektgrössen und Formen. Die zweite Methode unterscheidet mehrere definierte Objektklassen. Zudem schätzt sie die räumliche Tiefe in einer zweieinhalb-dimensionalen (2.5D) Interpretation des Bildes mithilfe einer einzelnen kalibrierten Kamera. Damit wird die Verfolgung weniger fehleranfällig bei vielen überlappenden Objekten auf engem Raum. Bei beiden Verfolgungsmethoden werden die Bildpunkte segmentiert in Vordergrund und Hintergrund. Dabei werden Unterschiede in Bewegung und Aussehen der Objekte als Bayesische Wahrscheinlichkeiten interpretiert und klassifiziert. Beide Verfolgungsmethoden arbeiten in Echtzeit, passen sich laufend den Lichtverhältnissen an und erkennen Verdeckungen zwischen Objekten. Daraus entstanden mehrere Prototypsysteme. Zum einen werden Systeme zur kognitiven Videoüberwachung auf verschiedenen Auflösungsstufen präsentiert. Zum anderen wird mit einem Prototyp untersucht, wie die Privatsphäre trotz Videoüberwachung wiederhergestellt werden kann. Zusätzlich zu den Systemen beschreibt diese Arbeit eine hierfür geeignete quantitative Bewertungs- und Vergleichsmethode. Dabei werden konkrete ein-
5 iv ZUSAMMENFASSUNG zelne Ereignisse mit semantischer Bedeutung aus den Resultaten der Objektverfolgungsmethoden gefiltert und deren Erkennungsrate gemessen. Vergleiche zwischen mehreren modernen Objektverfolgungsmethoden und menschlich annotierten Resultaten wurden auf mehreren bekannten Videosequenzen durchgeführt.
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