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info.json
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{
"abstract": "Sparsity has been showed to be one of the most important properties for visual recognition purposes. In this paper we show that sparse representation plays a fundamental role in achieving one-shot learning and real-time recognition of actions. We start off from RGBD images, combine motion and appearance cues and extract state-of-the-art features in a computationally efficient way. The proposed method relies on descriptors based on 3D Histograms of Scene Flow (3DHOFs) and Global Histograms of Oriented Gradient (GHOGs); adaptive sparse coding is applied to capture high-level patterns from data. We then propose a simultaneous on-line video segmentation and recognition of actions using linear SVMs. The main contribution of the paper is an effective real-time system for one-shot action modeling and recognition; the paper highlights the effectiveness of sparse coding techniques to represent 3D actions. We obtain very good results on three different data sets: a benchmark data set for one-shot action learning (the ChaLearn Gesture Data Set), an in-house data set acquired by a Kinect sensor including complex actions and gestures differing by small details, and a data set created for human-robot interaction purposes. Finally we demonstrate that our system is effective also in a human-robot interaction setting and propose a memory game, \u00e2\u0080\u009cAll Gestures You Can\u00e2\u0080\u009d, to be played against a humanoid robot.",
"authors": [
"Sean Ryan Fanello",
"Ilaria Gori",
"Giorgio Metta",
"Francesca Odone"
],
"id": "fanello13a",
"issue": 80,
"pages": [
2617,
2640
],
"title": "Keep It Simple And Sparse: Real-Time Action Recognition",
"volume": 14,
"year": 2013
}