PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects

Stefan Duffner, Christophe Garcia; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 2480-2487

Abstract


In this paper, we present a novel algorithm for fast tracking of generic objects in videos. The algorithm uses two components: a detector that makes use of the generalised Hough transform with pixel-based descriptors, and a probabilistic segmentation method based on global models for foreground and background. These components are used for tracking in a combined way, and they adapt each other in a co-training manner. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging standard videos, and outperforms state-of-theart tracking methods designed for the same task. Finally, the proposed models allow for an extremely efficient implementation, and thus tracking is very fast.

Related Material


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[bibtex]
@InProceedings{Duffner_2013_ICCV,
author = {Duffner, Stefan and Garcia, Christophe},
title = {PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}