A motion model based on recurrent neural networks for visual object tracking
作者:
Highlights:
• Developing an LSTM-based motion model for single-object tracking.
• End-to-end training using different combinations of processed motion features.
• Incorporating the developed model into existing single-object tracking algorithms.
• Dynamically setting search region location.
• Demonstrating considerable improvement in tracking performance.
摘要
•Developing an LSTM-based motion model for single-object tracking.•End-to-end training using different combinations of processed motion features.•Incorporating the developed model into existing single-object tracking algorithms.•Dynamically setting search region location.•Demonstrating considerable improvement in tracking performance.
论文关键词:Single-object tracking,Motion model,Long short-term memory,Recurrent neural network
论文评审过程:Received 15 June 2022, Accepted 7 August 2022, Available online 14 August 2022, Version of Record 24 August 2022.
论文官网地址:https://doi.org/10.1016/j.imavis.2022.104533