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

Hong Zhibin (1) , Zhe Chen (1) , Chaohui Wang (2) , Xue Mei (3) , Danil Prokhorov (3) , Dacheng Tao (1)

1 Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology
2 LIGM - Laboratoire d'Informatique Gaspard-Monge
3 Toyota Research Institute

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.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

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. ⟨10.1109/CVPR.2015.7298675⟩. ⟨hal-01155588⟩