Unmanned aerial vehicle object tracking by correlation filter with adaptive appearance model

Xizhe Xue, Ying Li, Qiang Shen

Research output: Contribution to journalArticlepeer-review

18 Citations (SciVal)
138 Downloads (Pure)


With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach.
Original languageEnglish
Article number2751
Issue number9
Publication statusPublished - 21 Aug 2018


  • Adaptive appearancemodel
  • Cellular automata
  • Correlation filter
  • UAV video
  • Visual tracking


Dive into the research topics of 'Unmanned aerial vehicle object tracking by correlation filter with adaptive appearance model'. Together they form a unique fingerprint.

Cite this