Soft-assigned bag of features for object tracking

Tongwei Ren, Zhongyan Qiu, Yan Liu, Tong Yu, Jia Bei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)189-205
Number of pages17
JournalMultimedia Systems
Issue number2
Publication statusPublished - 1 Jan 2014


  • Bag of features
  • Object tracking
  • Soft assignment
  • Visual representation

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


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