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Human-action recognition through local spatio-temporal features have been widely applied because of their simplicity and its reasonable computational complexity. The most common method to represent such features is the well-known Bag-of-Words approach, which turns a Multiple-Instance Learning problem into a supervised learning one, which can be addressed by a standard classifier. In this paper, a...
Augmented reality is becoming more and more popular due to the countless number of practical applications. A key element is the understanding of the scene and the involved human activities to be able to offer a rich interaction with the world via virtual actions and elements. For this purpose, a new vision-based human-action recognition module has been developed to be integrated with the new generation...
A novel and robust vision-based human-machine interface system to naturally interact with computers/smart devices is proposed. The key contribution is the introduction of a Compressive Sensing technique to largely reduce the dimensionality of highly discriminative feature descriptors (computed from depth imagery), which originally have an excessive and inoperative high dimension to be applied to a...
A robust hand-gesture recognition system based on a novel descriptor called Volumetric Spatiogram of Local Binary Patterns is presented, which allows a more natural input interface for simulating a mouse. The recognition stage based on Support Vector Machines triggers different mouse functions depending on the recognized gesture.
A real-time surveillance system for IP network cameras is presented. Motion, part-body, and whole-body detectors are efficiently combined to generate robust and fast detections, which feed multiple compressive trackers. The generated trajectories are then improved using a re-identification strategy for long term operation.
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