MUlti-Store Tracker (MUSTer): a Cognitive Psychology Inspired Approach to Object Tracking

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.
Document type :
Conference papers
Complete list of metadatas

https://hal-upec-upem.archives-ouvertes.fr/hal-01155588
Contributor : Chaohui Wang <>
Submitted on : Monday, June 8, 2015 - 4:07:53 PM
Last modification on : Monday, March 11, 2019 - 11:14:03 AM
Long-term archiving on : Tuesday, September 15, 2015 - 6:23:45 AM

File

MUlti-Store Tracker (MUSTer) a...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01155588, version 1

Citation

Hong Zhibin, Zhe Chen, Chaohui Wang, Xue Mei, Danil Prokhorov, et al.. MUlti-Store Tracker (MUSTer): a Cognitive Psychology Inspired Approach to Object Tracking. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2015, Boston, United States. ⟨hal-01155588⟩

Share

Metrics

Record views

573

Files downloads

553