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United Kingdom (2013)" 3D Human Tracking from Depth Cue in a Buying Behavior Analysis Context
, 2013
"... Abstract. This paper presents a real time approach to track the human body pose in the 3D space. For the buying behavior analysis, the camera is placed on the top of the shelves, above the customers. In this top view, the markerless tracking is harder. Hence, we use the depth cue provided by the kin ..."
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Abstract. This paper presents a real time approach to track the human body pose in the 3D space. For the buying behavior analysis, the camera is placed on the top of the shelves, above the customers. In this top view, the markerless tracking is harder. Hence, we use the depth cue provided by the kinect that gives discriminative features of the pose. We introduce a new 3D model that are fitted to these data in a particle filter framework. First the head and shoulders position is tracked in the 2D space of the acquisition images. Then the arms poses are tracked in the 3D space. Finally, we demonstrate that an efficient implementation provides a real-time system.
3D Human Tracking from Depth Cue in a Buying Behavior Analysis Context
"... Abstract. This paper presents a real time approach to track the human body pose in the 3D space. For the buying behavior analysis, the camera is placed on the top of the shelves, above the customers. In this top view, the markerless tracking is harder. Hence, we use the depth cue provided by the kin ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract. This paper presents a real time approach to track the human body pose in the 3D space. For the buying behavior analysis, the camera is placed on the top of the shelves, above the customers. In this top view, the markerless tracking is harder. Hence, we use the depth cue provided by the kinect that gives discriminative features of the pose. We introduce a new 3D model that are fitted to these data in a particle filter framework. First the head and shoulders position is tracked in the 2D space of the acquisition images. Then the arms poses are tracked in the 3D space. Finally, we demonstrate that an efficient implementation provides a real-time system.
