Mean shift tracker combined with online learning-based detector and Kalman filtering for real-time tracking
作者：
Highlights：
• A new tracking method combining a mean shift tracker with an online learning-based detector and a Kalman filter.
• A Mahalanobis distance-based validation region for reduction of calculation time.
• Target model update scheme for long-term tracking.
• Experiments on eight challenging video sequences to compare against state-of-the-art methods.
• Demonstration of superiority in term of accuracy and speed.
摘要
•A new tracking method combining a mean shift tracker with an online learning-based detector and a Kalman filter.•A Mahalanobis distance-based validation region for reduction of calculation time.•Target model update scheme for long-term tracking.•Experiments on eight challenging video sequences to compare against state-of-the-art methods.•Demonstration of superiority in term of accuracy and speed.
论文关键词：Mean shift tracker,Re-initialization,Detector,Validation region,Mahalanobis distance,Kalman filter
论文评审过程：Received 24 November 2016, Revised 22 February 2017, Accepted 26 February 2017, Available online 28 February 2017, Version of Record 9 March 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.02.043
