Structured Light (SL) patterns generated based on pseudo-random arrays are widely used for single-shot 3D reconstruction using projector-camera systems. These SL images consist of a set of tags with different appearances, where these patterns will be projected on a target surface, then captured by a camera and decoded. The precision of localizing these tags from captured camera images affects the quality of the pixel-correspondences between the projector and the camera, and consequently that of the derived 3D shape. In this paper, we incorporate a quadrilateral representation for the detected SL tags that allows the construction of robust and accurate pixel-correspondences and the application of a spatial rectification module that leads to high tag classification accuracy. When applying the proposed method to single-shot 3D reconstruction, we show the effectiveness of this method over a baseline in estimating denser and more accurate 3D point-clouds.
The past decade has witnessed significant progress in object detection and tracking in videos. In this paper, we present a model for collaboration between a pre-trained object detector and multiple single object trackers in the particle filter tracking framework. For each frame, we construct an association between the trackers and the detections, and when a tracker is successfully associated to a detection, we treat this detection as the key-sample for this tracker. We present a dual motion model that incorporates the associated detections with the object dynamics. Then, a likelihood function provides different weights for the propagated and the newly created particles, reducing the effect of false positives and missed detections in the tracking process. In addition, we use generative and discriminative appearance models to maximize the appearance variation among the targets. The performance of the proposed algorithm compares favorably with that of the state-of-the-art approaches on three public sequences.
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