Robust correlation filter tracking with multi-scale spatial view

Yafu Xiao, Jing Li*, Bo Du, Jia Wu, Xuefei Li, Jun Chang, Yifei Zhou

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Citations (Scopus)


With extensive applications, visual tracking has already become one of the most important research focuses in computer vision. Due to such interference as serious occlusion or severe illumination change and so on, the appearance model of the target tends to vary heavily, posing great challenges on tracking. However, a majority of existing tracking methods have difficulties in detecting the above interference under the single spatial view, affecting the performance of tracking method apparently. In this paper, a robust correlation filter tracking method with multi-scale spatial view (RCFMSV) is proposed in which a group of multi-scale spatial filters of different view areas is established. There are two models in RCFMSV, one is detection model of multi-scale spatial view (DMMSV), which is responsible for the interference detection with the help of different sensitivity of the spatial view in different scales. The other is on-line location model of multi-scale spatial view (On-line LMMSV), which is mainly used to perform collaborative location by introducing the method of pre-location and adopting the multi-scale spatial view around the target as a reference to realize a more accurate tracking method. Extensive tracking experiments have been conducted on the proposed algorithm in object tracking benchmark and detailed comparative analysis between this algorithm and the state-of-the-art methods also have been made. It is confirmed by the experiments and analysis that the RCFMSV tracking method proposed in our work is competitive with the state-of-the-art methods in tracking performance.

Original languageEnglish
Pages (from-to)119-140
Number of pages22
Publication statusPublished - 17 Sep 2019


  • Correlation filter
  • Kernel-based filter
  • Multi-scale spatial view
  • Visual tracking

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