A labeled random finite set online multi-object tracker for video data
作者：
Highlights：
• The proposed filter addresses occlusions and detection loss that exploits the advantages of both detection-based and TBD approaches to improve performance while reducing the computational cost.
• In a single Bayesian recursion the filter seamlessly integrates state estimation, track management, clutter rejection, detection loss and occlusion handling as well as prior knowledge that detection loss in the middle of the scene are likely to be due to occlusions.
• Tracking performance is compared to state-of-the-art algorithms on simulated data and well-known benchmark video datasets.
摘要
•The proposed filter addresses occlusions and detection loss that exploits the advantages of both detection-based and TBD approaches to improve performance while reducing the computational cost.•In a single Bayesian recursion the filter seamlessly integrates state estimation, track management, clutter rejection, detection loss and occlusion handling as well as prior knowledge that detection loss in the middle of the scene are likely to be due to occlusions.•Tracking performance is compared to state-of-the-art algorithms on simulated data and well-known benchmark video datasets.
论文关键词：Online multi-object tracking,Track-before-detect,Random finite set
论文评审过程：Received 13 April 2018, Revised 4 February 2019, Accepted 7 February 2019, Available online 7 February 2019, Version of Record 13 February 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.02.004
