Publications

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication


Year(s) from:  to 
Author:
Keywords (separated by spaces):

Dynamic Objectness for Adaptive Tracking

Severin Stalder, Helmut Grabner, Luc Van Gool
Asian Conference on Computer Vision
2012

Abstract

A fundamental problem of object tracking is to adapt to unseen views of the object while not getting distracted by other objects. We introduce Dynamic Objectness in a discriminative tracking framework to sporadically re-discover the tracked object based on motion. In doing so, drifting is effectively limited since tracking becomes more aware of objects as independently moving entities in the scene. The approach not only follows the object, but also the background to not easily adapt to other distracting objects. Finally, an appearance model of the object is incrementally built for an eventual re-detection after a partial or full occlusion. We evaluated it on several well-known tracking sequences and demonstrate results with superior accuracy, especially in difficult sequences with changing aspect ratios, varying scale, partial occlusion and non-rigid objects.


Download in pdf format
@InProceedings{eth_biwi_00979,
  author = {Severin Stalder and Helmut Grabner and Luc Van Gool},
  title = {Dynamic Objectness for Adaptive Tracking},
  booktitle = {Asian Conference on Computer Vision},
  year = {2012},
  keywords = {}
}