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Object Fow: Learning object displacement

C. Lalos, H. Grabner, L. Van Gool, and T. Varvarigou
In Proceeding IEEE Workshop on Visual Surveillance
2010

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.


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@InProceedings{eth_biwi_00756,
  author = {C. Lalos and H. Grabner and L. Van Gool and and T. Varvarigou},
  title = {Object Fow: Learning object displacement},
  booktitle = {In Proceeding IEEE Workshop on Visual Surveillance},
  year = {2010},
  keywords = {}
}