| Authors |
Schulter Samuel, Leistner Christian, Roth Peter M., Luc Van Gool, Bischof Horst |
| Appeared in |
Proceedings of British Machine Vision Conference (BMVC) |
| Date |
2011 |
| Abstract |
Hough forests have emerged as a powerful and versatile method, which achieves
state-of-the-art results on various computer vision applications, ranging from object detection over pose estimation to action recognition. The original method operates in offline mode, assuming to have access to the entire training set at once. This limits its
applicability in domains where data arrives sequentially or when large amounts of data
have to be exploited. In these cases, on-line approaches naturally would be beneficial.
To this end, we propose an on-line extension of Hough forests, which is based on the
principle of letting the trees evolve on-line while the data arrives sequentially, for both
classification and regression. We further propose a modified version of off-line Hough
forests, which only needs a small subset of the training data for optimization. In the
experiments, we show that using these formulations, the classification results of classic
Hough forests could be reached or even outperformed, while being orders of magnitudes
faster. Furthermore, our method allows for tracking arbitrary objects without requiring
any prior knowledge. We present state-of-the-art tracking results on publicly available
data sets. |
| Link |
PDF |
