International audienceThis paper presents a novel approach for object identification and steady tracking in mobile augmented reality applications. First, the system identifies the object of interest using the KAZE algorithm. Then, the target tracking is enabled with the optical flow throughout the camera instant video stream. Further, the camera pose is determined by estimating the key transformation relating the camera reference frame according to the world coordinate system. Therefore, the visual perception is augmented with 3D virtual graphics overlaid on target object within the scene images. Finally, experiments are conducted to evaluate the system performances in terms of accuracy, robustness and computational efficiency as wel
International audienceThis paper presents a novel approach for object recognition in extended image databases using a mobile client server architecture. The proposed approach relies upon feature detection and description to characterize textured objects within the image. The similarity search is performed on descriptor arrays by computing the distance between the query descriptor compared with reference descriptors extracted offline. The key contributions of the approach are the high accuracy, the time-effectiveness and the scalability of the method towards large image datasets. The developed method is first, integrated on a mobile platform and, then, deployed on a client server architecture to deal with high volume image galleries. Experiments are performed to evaluate the performances of the system in real-life environment conditions and the obtained results demonstrate the relevance of the proposed approac
In this work, we proposed and developed an approach for markerless object detection and registration in augmented reality. Our system enables the superimposition of videos or 3D graphics on natural objects in a real-time tracking process. The object of interest is detected within a sequence of images using the feature points and their invariant descriptors. The matching process between the test image and the training images is calculated using a 2D homography to generate the projective transformation for the video registration part. Further, the 3D graphic is overlaid into the scene by estimating the real camera pose and solving transformations relating the virtual and the real reference frames. The conducted experiments provided accurate and time effective results. Our approach detects and tracks markerless objects in real-time and enables video and 3D models registration for augmented reality experiences.
This paper presents an approach for tracking natural objects in augmented reality applications. The targets are detected and identified using a markerless approach relying upon the extraction of image salient features and descriptors. The method deals with large image databases using a novel strategy for feature retrieval and pairwise matching. Furthermore, the developed method integrates a real-time solution for 3D pose estimation using an analytical technique based on camera perspective transformations. The algorithm associates 2D feature samples coming from the identification part with 3D mapped points of the object space. Next, a sampling scheme for ordering correspondences is carried out to establishing the 2D/3D projective relationship. The tracker performs localization using the feature images and 3D models to enhance the scene view with overlaid graphics by computing the camera motion parameters. The modules built within this architecture are deployed on a mobile platform to provide an intuitive interface for interacting with the surrounding real world. The system is experimented and evaluated on challenging scalable image dataset and the obtained results demonstrate the effectiveness of the approach towards versatile augmented reality applications.
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