Online CNN-based multiple object tracking with enhanced model updates and identity association
作者：
Highlights：
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• An online MOT method using multiple CNN-based SOT trackers is proposed, where each target is associated with one unique network tracker. The network tracker has been trained to be invariant to scale and orientation changes, which is suitable for tracking task.
• Two online model update schemes: 1) the incremental update and 2) the refresh update, have been designed to work together to online train a powerful yet efficient dynamic target model in a complicated MOT environment.
• In order to assign the correct identity to the target, an ID association step is proposed after the individual target tracking. Multiple feature cues have been utilized, including deep features from different layers in the network and motion information.
摘要
•An online MOT method using multiple CNN-based SOT trackers is proposed, where each target is associated with one unique network tracker. The network tracker has been trained to be invariant to scale and orientation changes, which is suitable for tracking task.•Two online model update schemes: 1) the incremental update and 2) the refresh update, have been designed to work together to online train a powerful yet efficient dynamic target model in a complicated MOT environment.•In order to assign the correct identity to the target, an ID association step is proposed after the individual target tracking. Multiple feature cues have been utilized, including deep features from different layers in the network and motion information.
论文关键词：Online multiple object tracking,Convolutional neural network tracker,Model update,Identity association
论文评审过程：Received 29 November 2017, Revised 9 May 2018, Accepted 9 May 2018, Available online 12 May 2018, Version of Record 24 May 2018.
论文官网地址：https://doi.org/10.1016/j.image.2018.05.008
