Distractor-aware discrimination learning for online multiple object tracking
作者：
Highlights：
• A distractor-aware discrimination learning model is proposed to facilitate online multi-object tracking to better differentiate one target from other targets and semantic backgrounds in the scenes.
• A relational attention learning mechanism is introduced to handle appearance variations of targets caused by large pose variations, object occlusions, and target interactions.
• A multi-stage tracking strategy is established within a temporal sliding window which leverages the object detection responses and tracker predictions to deal with trajectory drifting.
• Extensive experimental analyses and evaluations on the widely used challenging MOT16 and MOT17 benchmarks demonstrate the effectiveness of the proposed approach.
摘要
•A distractor-aware discrimination learning model is proposed to facilitate online multi-object tracking to better differentiate one target from other targets and semantic backgrounds in the scenes.•A relational attention learning mechanism is introduced to handle appearance variations of targets caused by large pose variations, object occlusions, and target interactions.•A multi-stage tracking strategy is established within a temporal sliding window which leverages the object detection responses and tracker predictions to deal with trajectory drifting.•Extensive experimental analyses and evaluations on the widely used challenging MOT16 and MOT17 benchmarks demonstrate the effectiveness of the proposed approach.
论文关键词：Multi-object tracking,Distractor-aware discrimination learning,Relational attention learning
论文评审过程：Received 12 August 2019, Revised 14 April 2020, Accepted 22 June 2020, Available online 24 June 2020, Version of Record 30 June 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107512
