In long-term visual object tracking, the tracking model would be prone to drift or corruption and the tracker can hardly catch the target again after tracking failures. A set of novel strategies for long-term tracking is proposed to solve these problems. First, a simple and efficient method is proposed to calculate the tracking confidence of Staple, a well-known tracker based on correlation filers. A model update mechanism is then developed to prevent model corruption. Furthermore, an online Support Vector Machine (SVM) classifier is trained to re-detect the object in case of unreliable tracking result. By means of intermittent sampling in the re-detection stage, the computational efficiency and the re-detection reliability are greatly improved. The combination of these new components in multi-stages spawns a real-time, accurate and robust tracker for long-term video. Experimental results demonstrate that our tracker, operating at a speed of 30 FPS, performs superiorly against some competitive trackers on robustness and accuracy, especially when the target encounters occlusion, severe deformation and out-of-view.
KEYWORDS: 3D modeling, Control systems, Systems modeling, Laser scanners, 3D scanning, Computer aided design, Calibration, Solid modeling, Neurons, Visual process modeling
The technology of laser projection positioning is widely applied in advanced manufacturing fields (e.g. composite plying, parts location and installation). In order to use it better, a laser projection positioning (LPP) system is designed and implemented. Firstly, the LPP system is built by a laser galvanometric scanning (LGS) system and a binocular vision system. Applying Single-hidden Layer Feed-forward Neural Network (SLFN), the system model is constructed next. Secondly, the LGS system and the binocular system, which are respectively independent, are integrated through a datadriven calibration method based on extreme learning machine (ELM) algorithm. Finally, a projection positioning method is proposed within the framework of the calibrated SLFN system model. A well-designed experiment is conducted to verify the viability and effectiveness of the proposed system. In addition, the accuracy of projection positioning are evaluated to show that the LPP system can achieves the good localization effect.
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