Structured light 3D vision inspection is widely used in various 3D surface reconstruction techniques. However, there are still two problems to be solved. The first problem is that if the relative position between the camera and laser projector is changed, the system has to be calibrated again. The second problem is that a turntable or a linear positioning system is needed in order to obtain the complete 3D reconstruction data in conventional methods. To overcome these problems, we proposed a novel design using a dual semicircular planar target and a rotating laser projector to perform calibration and 3D reconstruction simultaneously. The system contains two semicircular planar targets hollowed out at center which are installed in up and down position. The middle part of the light stripe can pass through the blank part of semicircular planar target and then cast on the target object, while the rest of the light stripe will cast on the semicircular planar target. The laser projector is rotated 360 degrees along an axis perpendicular to the planar target plane to get the complete 3D surface reconstruction of the target object. Experiments are conducted to validate the flexibility and accuracy of the system.
Object tracking is a challenging task in computer vision. Most state-of-the-art methods maintain an object model and update the object model by using new examples obtained incoming frames in order to deal with the variation in the appearance. It will inevitably introduce the model drift problem into the object model updating frame-by-frame without any censorship mechanism. In this paper, we adopt a multi-expert tracking framework, which is able to correct the effect of bad updates after they happened such as the bad updates caused by the severe occlusion. Hence, the proposed framework exactly has the ability which a robust tracking method should process. The expert ensemble is constructed of a base tracker and its formal snapshot. The tracking result is produced by the current tracker that is selected by means of a simple loss function. We adopt an improved compressive tracker as the base tracker in our work and modify it to fit the multi-expert framework. The proposed multi-expert tracking algorithm significantly improves the robustness of the base tracker, especially in the scenes with frequent occlusions and illumination variations. Experiments on challenging video sequences with comparisons to several state-of-the-art trackers demonstrate the effectiveness of our method and our tracking algorithm can run at real-time.