HMMs are widely used in action and gesture recognition due to their implementation simplicity, low computational requirement, scalability and high parallelism. They have worth performance even with a limited training set. All these characteristics are hard to find together in other even more accurate methods. In this paper, we propose a novel doublestage classification approach, based on Multiple Stream Discrete Hidden Markov Models (MSD-HMM) and 3D skeleton joint data, able to reach high performances maintaining all advantages listed above. The approach allows both to quickly classify presegmented gestures (offline classification), and to perform temporal segmentation on streams of gestures (online classification) faster than real time. We test our system on three public datasets, MSRAction3D, UTKinect-Action and MSRDailyAction, and on a new dataset, Kinteract Dataset, explicitly created for Human Computer Interaction (HCI). We obtain state of the art performances on all of them.

Fast gesture recognition with Multiple StreamDiscrete HMMs on 3D Skeletons / Borghi, Guido; Vezzani, Roberto; Cucchiara, Rita. - (2016). ((Intervento presentato al convegno 23rd International Conference on Pattern Recognition tenutosi a Cancun nel Dec 4-8, 2016 [10.1109/ICPR.2016.7899766].

Fast gesture recognition with Multiple StreamDiscrete HMMs on 3D Skeletons

BORGHI, GUIDO;VEZZANI, Roberto;CUCCHIARA, Rita
2016-01-01

Abstract

HMMs are widely used in action and gesture recognition due to their implementation simplicity, low computational requirement, scalability and high parallelism. They have worth performance even with a limited training set. All these characteristics are hard to find together in other even more accurate methods. In this paper, we propose a novel doublestage classification approach, based on Multiple Stream Discrete Hidden Markov Models (MSD-HMM) and 3D skeleton joint data, able to reach high performances maintaining all advantages listed above. The approach allows both to quickly classify presegmented gestures (offline classification), and to perform temporal segmentation on streams of gestures (online classification) faster than real time. We test our system on three public datasets, MSRAction3D, UTKinect-Action and MSRDailyAction, and on a new dataset, Kinteract Dataset, explicitly created for Human Computer Interaction (HCI). We obtain state of the art performances on all of them.
23rd International Conference on Pattern Recognition
Cancun
Dec 4-8, 2016
Borghi, Guido; Vezzani, Roberto; Cucchiara, Rita
Fast gesture recognition with Multiple StreamDiscrete HMMs on 3D Skeletons / Borghi, Guido; Vezzani, Roberto; Cucchiara, Rita. - (2016). ((Intervento presentato al convegno 23rd International Conference on Pattern Recognition tenutosi a Cancun nel Dec 4-8, 2016 [10.1109/ICPR.2016.7899766].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1103864
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