A. Kerboua
ABSTRACT
The current state of art 1 allows achieving high action recognition accuracy but only after processing the entire video sequence, however for security issues, it is primordial to detect dangerous behaviors occurrence as soon as possible allowing early warnings. In this paper, we present a human activity recognition method using skeleton information provided by an RGB-D sensor by proposing a new descriptor modelling dynamic relation between 3D locations of skeleton joints, expressed in distance and spherical coordinates between the 20 available joints. First, we perform preprocessing step to recover missing skeleton information over frames, so, we normalize skeleton joints and apply a PCA dimension reduction to remove noisy information and enhance recognition accuracy while improving calculation and decision time. We also study the accuracy of the proposed descriptor calculated over limited few first frames to perform early action detection by specifying multiclass SVM classifier. We test this approach on two datasets, MSR Daily Activity 3D and our own dataset called INDACT; experimental evaluation shows that proposed approach can robustly classify actions outperforming state-of-art and maintain good accuracy score even using limited frame number.
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