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):

Cascaded Confidence Filtering for Improved Tracking-by-Detection

S. Stalder, H. Grabner, and L. Van Gool
In Proceedings European Conference on Computer Vision (ECCV)
2010

Abstract

We propose a novel approach to increase the robustness of object detection algorithms in surveillance scenarios. The cascaded confidence filter successively incorporates constraints on the size of the objects, on the preponderance of the background and on the smoothness of trajectories. In fact, the continuous detection confidence scores are analyzed locally to adapt the generic detector to the specific scene. The approach does not learn specific object models, reason about complete trajectories or scene structure, nor use multiple cameras. Therefore, it can serve as preprocessing step to robustify many tracking-by-detection algorithms. Our real-world experiments show significant improvements, especially in the case of partial occlusions, changing backgrounds, and similar distractors.


Download in pdf format
@InProceedings{eth_biwi_00753,
  author = {S. Stalder and H. Grabner and and L. Van Gool},
  title = {Cascaded Confidence Filtering for Improved Tracking-by-Detection},
  booktitle = {In Proceedings European Conference on Computer Vision (ECCV)},
  year = {2010},
  keywords = {}
}