Adaptive Compressive Tracking based on Locality Sensitive Histograms
作者：
Highlights：
• The Haar-like features generated from LSH feature image are used to represent the target appearance model, which can handle illumination changes.
• A color attributes tracker is employed to predict the target position and to re-build the new discriminant function.
• A novel model updating mechanism is proposed to maintain the stability of the features while avoiding noisy.
• A trajectory rectification method is adopted to make the finally estimated location more accurate and avoid drifting.
• The proposed tracker outperforms state-of-the-art trackers over the recent challenging tracking benchmark data set.
摘要
•The Haar-like features generated from LSH feature image are used to represent the target appearance model, which can handle illumination changes.•A color attributes tracker is employed to predict the target position and to re-build the new discriminant function.•A novel model updating mechanism is proposed to maintain the stability of the features while avoiding noisy.•A trajectory rectification method is adopted to make the finally estimated location more accurate and avoid drifting.•The proposed tracker outperforms state-of-the-art trackers over the recent challenging tracking benchmark data set.
论文关键词：Compressive tracking,Locality Sensitive Histograms,Tracking-by-detection
论文评审过程：Received 16 December 2016, Revised 12 June 2017, Accepted 4 July 2017, Available online 14 July 2017, Version of Record 17 August 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.07.006
