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Online structured-output learning for real-time object tracking and detection
Sam Hare · Amir Saffari · Philip Torr

Tue Dec 13 08:45 AM -- 02:59 PM (PST) @ None

This demonstration will show two applications of online structured-output learning for real-time computer vision tasks. The first application is for 2D tracking of arbitrary objects, and uses an online kernelized structured-output SVM on a budget to provide robust adaptive tracking. The second application is for real-time 3D object detection. There has been much work recently in the computer vision community on highly efficient interest-point matching techniques, which allow real-time detection and pose estimation of objects. Existing methods store a database of interest-points extracted from reference views of the object, which are then matched in a nearest-neighbor fashion. We propose a different approach, which learns these descriptors discriminatively online in a structured-output SVM framework. This means the descriptors are optimized for detecting the target object in a given environment, providing improved detection performance. This work has been carried out in collaboration with Sony Computer Entertainment Europe (SCEE), and we have integrated our approach into a real-time simultaneous localization and mapping (SLAM) system running on the forthcoming Sony PlayStation Vita portable games console, where it provides the relocalization component for recovery from tracking failure.

Author Information

Sam Hare (Oxford Brookes University)
Amir Saffari (BenevolentAI)
Philip Torr (Oxford University)

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