Object flow: learning object displacement

Authors:
Constantinos Lalos;Helmut Grabner;Luc Van Gool;Theodora Varvarigou
Affiliations:
School of Electrical & Computer Engineering, NTUA, Greece;Computer Vision Laboratory, ETH Zurich, Switzerland;Computer Vision Laboratory, ETH Zurich, Switzerland and ESAT-PSI/IBBT, K.U. Leuven, Belgium;School of Electrical & Computer Engineering, NTUA, Greece
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Year:
2010

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Abstract

Modelling the dynamic behaviour of moving objects is one of the basic tasks in computer vision. In this paper, we introduce the Object Flow, for estimating both the displacement and the direction of an object-of-interest. Compared to the detection and tracking techniques, our approach obtains the object displacement directly similar to optical flow, while ignoring other irrelevant movements in the scene. Hence, Object Flow has the ability to continuously focus on a specific object and calculate its motion field. The resulting motion representation is useful for a variety of visual applications (e.g., scene description, object tracking, action recognition) and it cannot be directly obtained using the existing methods.