EACOFT: An energy-aware correlation filter for visual tracking

作者：

Highlights：

• Energy-aware-correlation-filter tracker to adaptively adjust the target for tracking.

• New strategy to reject low quality samples and ensure model discriminant ability.

• Combining bottom-up and top-down optimal strategy for training and robust tracking.

• Outperform many state-of-the-art trackers on several challenging datasets.

摘要

•Energy-aware-correlation-filter tracker to adaptively adjust the target for tracking.•New strategy to reject low quality samples and ensure model discriminant ability.•Combining bottom-up and top-down optimal strategy for training and robust tracking.•Outperform many state-of-the-art trackers on several challenging datasets.

论文关键词：Visual tracking,Energy-aware correlation filter (EACOFT),Enhanced feature,Top-down and bottom-up strategy

论文评审过程：Received 12 March 2020, Revised 9 July 2020, Accepted 21 November 2020, Available online 8 December 2020, Version of Record 20 December 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107766