Robust correlation filter tracking based on response map analysis network
作者：
Highlights：
• We propose an effective way of evaluating the tracking confidence score of correlation filter utilizing CNN.
• We propose to learn multiple filters to exploit different features and adaptively adjust the update parameters according to confidence scores.
• We build a simple occlusion event model to detect heavy occlusion and recover target.
• The improved DCF tracker performs favorably compared to other SOTA trackers on several large-scale benchmarks.
摘要
•We propose an effective way of evaluating the tracking confidence score of correlation filter utilizing CNN.•We propose to learn multiple filters to exploit different features and adaptively adjust the update parameters according to confidence scores.•We build a simple occlusion event model to detect heavy occlusion and recover target.•The improved DCF tracker performs favorably compared to other SOTA trackers on several large-scale benchmarks.
论文关键词：Visual tracking,Discriminative correlation filter,Tracking confidence,Neuron network
论文评审过程：Received 21 December 2020, Revised 4 March 2022, Accepted 7 June 2022, Available online 15 June 2022, Version of Record 4 August 2022.
论文官网地址：https://doi.org/10.1016/j.image.2022.116768
