Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29376
Title: Soft-assigned bag of features for object tracking
Authors: Ren, T
Qiu, Z
Liu, Y 
Yu, T
Bei, J
Keywords: Bag of features
Object tracking
Soft assignment
Visual representation
Issue Date: 2014
Publisher: Springer Verlag
Source: Multimedia systems, 2014, v. 21, no. 2, p. 189-205 How to cite?
Journal: Multimedia Systems 
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.
URI: http://hdl.handle.net/10397/29376
ISSN: 0942-4962
DOI: 10.1007/s00530-014-0384-y
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