Abstract-The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways. The main elements of an integrated platform, which target tele-immersion and future 3D applications, are described in this paper, addressing the tasks of real-time capturing, robust 3D human shape/appearance reconstruction, and skeletonbased motion tracking. More specifically, initially, the details of a multiple RGB-depth (RGB-D) capturing system are given, along with a novel sensors' calibration method. A robust, fast reconstruction method from multiple RGB-D streams is then proposed, based on an enhanced variation of the volumetric Fourier transform-based method, parallelized on the Graphics Processing Unit, and accompanied with an appropriate texturemapping algorithm. On top of that, given the lack of relevant objective evaluation methods, a novel framework is proposed for the quantitative evaluation of real-time 3D reconstruction systems. Finally, a generic, multiple depth stream-based method for accurate real-time human skeleton tracking is proposed. Detailed experimental results with multi-Kinect2 data sets verify the validity of our arguments and the effectiveness of the proposed system and methodologies.
Abstract-In this paper, we propose a visual object tracking framework, which employs an appearance-based representation of the target object, based on local steering kernel descriptors and color histogram information. This framework takes as input the region of the target object in the previous video frame and a stored instance of the target object, and tries to localize the object in the current frame by finding the frame region that best resembles the input. As the object view changes over time, the object model is updated, hence incorporating these changes. Color histogram similarity between the detected object and the surrounding background is employed for background subtraction. Experiments are conducted to test the performance of the proposed framework under various conditions. The proposed tracking scheme is proven to be successful in tracking objects under scale and rotation variations and partial occlusion, as well as in tracking rather slowly deformable articulated objects.Index Terms-Color histograms, local steering kernels, visual object tracking.
Abstract-In this paper a novel method is introduced for propagating person identity labels on facial images extracted from stereo videos. It operates on image data with multiple representations and calculates a projection matrix that preserves locality information and a priori pairwise information, in the form of must-link and cannot-link constraints between the various data representations. The final data representation is a linear combination of the projections of all data representations. Moreover, the proposed method takes into account information obtained through data clustering. This information is exploited during the data propagation step in two ways: to regulate the similarity strength between the projected data and to indicate which samples should be selected for label propagation initialization. The performance of the proposed Multiple Locality Preserving Projections with Cluster-based Label Propagation (MLPP-CLP) method was evaluated on facial images extracted from stereo movies. Experimental results showed that the proposed method outperforms state of the art methods.
This paper extends the state of the art label propagation framework in the propagation of negative labels. More specifically, the state of the art label propagation methods propagate information of the form: "the sample i should be assigned the label k". The proposed method extends the state of the art framework by considering additional information of the form: "the sample i should not be assigned the label k". A theoretical analysis is presented in order to include negative label propagation in the problem formulation. Moreover, a method for selecting the negative labels in cases when they are not inherent from the data structure is presented. Furthermore, the incorporation of negative label information in two multi-graph label propagation methods is presented. Finally, a discussion on the proposed algorithm extension to out of sample data as well as scalability issues is presented. Experimental results in various scenarios showed that the incorporation of negative label information increases in all cases the classification accuracy of the state of the art.
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