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This study addresses the problem of efficiently combining the joint, RGB and depth modalities of the Kinect sensor in order to recognise human actions. For this purpose, a multi-layered fusion scheme concatenates different specific features, builds specialised local and global SVM models and then iteratively fuses their different scores. The authors essentially contribute in two levels: (i) they combine...
We introduce a new video contents description approach and use it for the purpose of coarse localization. It is based on a Bag of Words representation combining both space-time STIP features and semantic-context SSC features. We assume that adding semantic context encodes in a more efficient way the spatio-temporal information into video sequences. The resulting augmented descriptor is related to...
We present in this paper a new approach for human-action extraction and recognition in a multi-modal context. Our solution contains two modules. The first one applies temporal action segmentation by combining a heuristic analysis with augmented-joint description and SVM classification. The second one aims for a frame-wise action recognition using skeletal, RGB and depth modalities coupled with a label-grouping...
We present in this paper a novel solution for temporal segmentation of human gestures that takes advantage of the skeletal-joints streams offered by the Kinect sensor. Our contribution consist in introducing an improved skeletal representation and its usage in a multilayer motion delimitation that distinguishes the non-vocabulary actions. The evaluation of the solution is presented on a subset of...
This paper presents a solution capable of recognizing the facial expressions performed by a person's face and mapping them to a 3D face virtual model using the depth and RGB data captured from Microsoft's Kinect sensor. This solution starts by detecting the face and segmenting its regions, then, it identifies the actual expression using EigenFaces metrics on the RGB images and reconstructs the face...
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