Abstract:
This paper proposes a coarse-to-fine approach for fast image tracking. The tracking method is built based on correlation tracker which employs online learning and fast de...Show MoreMetadata
Abstract:
This paper proposes a coarse-to-fine approach for fast image tracking. The tracking method is built based on correlation tracker which employs online learning and fast detection by utilizing Fourier transform principles. Firstly, a small patch is extracted from a region near the tracked pixel. This patch is divided into a number of cells and then features are extracted from each cell, providing a set of training data. Together with the target values which are set as maximum at the center of the patch and getting smaller as the cell position getting farther from the center, a training is performed to determine the filter values. In the successive frame, the filter response is calculated to determine the position of the tracked pixel which is co-located with the maximum response of the filter. Since the features are extracted from cells, the new position of the tracked pixel is not precisely known. By employing a second detection at finer resolution within the corresponding cell, the ambiguity of the tracked pixel is eliminated. The proposed method was evaluated on a public dataset and the result shows that this strategy achieves a faster computation time compared to the baseline method.
Date of Conference: 23-26 October 2016
Date Added to IEEE Xplore: 22 December 2016
ISBN Information: