Visual Tracking Using Combining Motion Constraint Model and Online Multiple Instance Boost Random Ferns

2012 ◽  
Vol 263-266 ◽  
pp. 2385-2392
Author(s):  
He Rong Zheng ◽  
Ye Jue Huang
Keyword(s):  
Object Tracking ◽  
Tracking Error ◽  
Object Motion ◽  
Motion Constraints ◽  
Video Object Tracking ◽  
Iir Filter ◽  
Error Accumulation ◽  
Constraint Model ◽  
Constraints Model ◽  
Motion Constraint

Video object tracking is essential algorithm for computer vision applications. An object tracking algorithm using combining motion constraints model and online multiple instance boost random ferns is proposed, which use IIR filter to obtain online learning for random ferns, and the random ferns are selected by online multiple instance boosting to construct classifier of online multiple instance boost random ferns. To reduce effects of tracking error accumulation, object motion constraint model is constructed to constrain the results classified by online multiple instance boost random ferns to locate object correctly, and construct positive and negative set to online update the classifier. The experiment shows that the proposed method achieves competitive detection results, which are comparable with state-of-the-art methods.

scholarly journals High-Speed Train Tunnel Navigation Method Based on Integrated MIMU/ODO/MC Navigation

2021 ◽  
Vol 11 (8) ◽  
pp. 3680
Author(s):  
Zhenqian Sun ◽  
Kanghua Tang ◽  
Xueying Wang ◽  
Meiping Wu ◽  
Yan Guo
Keyword(s):  
High Speed ◽  
Satellite System ◽  
Measurement Unit ◽  
Mems Devices ◽  
High Speed Train ◽  
Motion Constraints ◽  
Constraint Model ◽  
Navigation Accuracy ◽  
Motion Constraint ◽  
Navigation Method

When a high-speed train is running in a tunnel, the global navigation satellite system (GNSS) signal is completely lost. Relying only on the inertial navigation system (INS) composed of Micro-electromechanical Systems (MEMS) devices leads to large navigation errors. To solve this problem, an integrated micro inertial measurement unit (MIMU), odometer (ODO), and motion constraint (MC) tunnel navigation method is proposed. This method first establishes a motion constraint model based on the installation angles of MIMU; secondly, the effect of turning on the motion constraint model and the odometer is analyzed and the use condition of the motion constraints is obtained; the installation angles of MIMU are then estimated when GNSS signal is good and the use condition of the motion constraints is met; finally, the forward speed measured by the odometer and the motion constraints are applied to suppress the error of the INS and improve the navigation accuracy in the tunnel. Based on this method, high-speed train navigation tests were carried out both in areal tunnel environment and in a case study with an artificially disconnected GNSS signal. The experimental results showed that the navigation accuracy of the train in the tunnel was significantly improved. Seamless navigation was achieved inside and outside the tunnel, which verified the effectiveness of the method.

Highly non-rigid video object tracking using segment-based object candidates

2016 ◽  
Vol 76 (7) ◽  
pp. 9565-9586 ◽  
Author(s):  
Cong Lin ◽  
Chi-Man Pun ◽  
Guoheng Huang
Keyword(s):  
Object Tracking ◽  
Video Object ◽  
Video Object Tracking
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