The ability to detect, localize and track the hands is crucial in many applications requiring the understanding of the person behavior, attitude and interactions. In particular, this is true for the automotive context, in which hand analysis allows to predict preparatory movements for maneuvers or to investigate the driver’s attention level. Moreover, due to the recent diffusion of cameras inside new car cockpits, it is feasible to use hand gestures to develop new Human-Car Interaction systems, more user-friendly and safe. In this paper, we propose a new dataset, called Turms, that consists of infrared images of driver’s hands, collected from the back of the steering wheel, an innovative point of view. The Leap Motion device has been selected for the recordings, thanks to its stereo capabilities and the wide view-angle. Besides, we introduce a method to detect the presence and the location of driver’s hands on the steering wheel, during driving activity tasks.

Hands on the wheel: a Dataset for Driver Hand Detection and Tracking / Borghi, Guido; Frigieri, Elia; Vezzani, Roberto; Cucchiara, Rita. - (2018), pp. 564-570. ((Intervento presentato al convegno 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 tenutosi a Xi'An nel 15 May 2018 [10.1109/FG.2018.00090].

Hands on the wheel: a Dataset for Driver Hand Detection and Tracking

Guido Borghi
;
Roberto Vezzani;Rita Cucchiara
2018-01-01

Abstract

The ability to detect, localize and track the hands is crucial in many applications requiring the understanding of the person behavior, attitude and interactions. In particular, this is true for the automotive context, in which hand analysis allows to predict preparatory movements for maneuvers or to investigate the driver’s attention level. Moreover, due to the recent diffusion of cameras inside new car cockpits, it is feasible to use hand gestures to develop new Human-Car Interaction systems, more user-friendly and safe. In this paper, we propose a new dataset, called Turms, that consists of infrared images of driver’s hands, collected from the back of the steering wheel, an innovative point of view. The Leap Motion device has been selected for the recordings, thanks to its stereo capabilities and the wide view-angle. Besides, we introduce a method to detect the presence and the location of driver’s hands on the steering wheel, during driving activity tasks.
13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Xi'An
15 May 2018
564
570
Borghi, Guido; Frigieri, Elia; Vezzani, Roberto; Cucchiara, Rita
Hands on the wheel: a Dataset for Driver Hand Detection and Tracking / Borghi, Guido; Frigieri, Elia; Vezzani, Roberto; Cucchiara, Rita. - (2018), pp. 564-570. ((Intervento presentato al convegno 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 tenutosi a Xi'An nel 15 May 2018 [10.1109/FG.2018.00090].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1156033
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