MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking
Zhibin Hong, Zhe Chen, Chaohui Wang, Xue Mei, Danil Prokhorov, Dacheng Tao
Abstract
Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking. Here, we adopt cognitive psychology principles to design a flexible representation that can adapt to changes in object appearance during tracking. Inspired by the well-known Atkinson-Shiffrin Memory Model, we propose MUlti-Store Tracker (MUSTer), a dual-component approach consisting of short- and long-term memory stores to process target appearance memories. A powerful and efficient Integrated Correlation Filter (ICF) is employed in the short-term store for short-term tracking. The integrated long-term component, which is based on keypoint matching-tracking and RANSAC estimation, can interact with the long-term memory and provide additional information for output control. MUSTer was extensively evaluated on the CVPR2013 Online Object Tracking Benchmark (OOTB) and ALOV++ datasets. The experimental results demonstrated the superior performance of MUSTer in comparison with other state-of-art trackers.
BibTeX
@inproceedings{cvpr2015_multistoretracke,
title = {MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking},
author = {Zhibin Hong and Zhe Chen and Chaohui Wang and Xue Mei and Danil Prokhorov and Dacheng Tao},
booktitle = {CVPR 2015},
year = {2015}
}