Abstract
Hard assignment-based bag of features (BoF) representation inevitably brings in quantization errors, which may lead to inaccuracy, even failure in object tracking. In this paper, we propose a novel soft-assigned BoF tracking approach, in which soft assignment is utilized to improve the robustness and discrimination of BoF representation. After labeling the tracked target, we first randomly sample the circle patches with adaptive size within and outside the labeled target, extract the local features from the patches, and construct the codebooks by k-means clustering. When tracking in a new frame, we generate the BoF representation of each candidate target, and select the most similar candidate target in the previous tracked result based on BoF representation. To improve tracking performance, we also continuously update the codebooks and refine the tracking results. Experiments show that our approach outperforms the state-of-the-art tracking methods under complex tracking conditions.
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Acknowledgments
The authors want to thank the anonymous reviews for their helpful suggestion, and Tao Huang for his contribution in experiment. This paper is supported by Natural Science Foundation of China (61202320), Research Project of Excellent State Key Laboratory (61223003), and Natural Science Foundation of Jiangsu Province (BK2012304).
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Ren, T., Qiu, Z., Liu, Y. et al. Soft-assigned bag of features for object tracking. Multimedia Systems 21, 189–205 (2015). https://doi.org/10.1007/s00530-014-0384-y
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DOI: https://doi.org/10.1007/s00530-014-0384-y