Description

The dataset captures 25 people preparing 2 mixed salads each and contains over 4h of annotated accelerometer and RGB-D video data. Annotated activities correspond to steps in the recipe and include activity class, activity phase (pre-/ core-/ post), and the ingredient acted upon. The dataset includes - RGB video data 640x480 pixels at 30 Hz - Depth maps 640x480 pixels at 30 Hz - 3-axis accelerometer data at 50 Hz of devices attached to a knife, a mixing spoon, a small spoon, a peeler, a glass, an oil bottle, and a pepper dispenser. - Synchronization parameters for temporal alignment of video and accelerometer data - Annotations as temporal intervals of pre- core- and post-phases of activities corresponding to steps in a recipeActivity recognition research has shifted focus from distinguishing full-body motion patterns to recognizing complex interactions of multiple entities. Manipulative gestures - characterized by interactions between hands, tools, and manipulable objects - frequently occur in food preparation, manufacturing, and assembly tasks, and have a variety of applications including situational support, automated supervision, and skill assessment. 50 Saladsdataset aims to stimulate research on recognizing manipulative gestures. It captures 25 people preparing 2 mixed salads each and contains over 4h of annotated accelerometer and RGB-D video data. Including detailed annotations, multiple sensor types, and two sequences per participant, the50 Saladsdataset may be used for research in areas such as activity recognition, activity spotting, sequence analysis, progress tracking, sensor fusion, transfer learning, and user-adaptation. The dataset includesRGB video data 640x480 pixels at 30 HzDepth maps 640x480 pixels at 30 Hz3-axis accelerometer data at 50 Hz of devices attached to a knife, a mixing spoon, a small spoon, a peeler, a glass, an oil bottle, and a pepper dispenser. Synchronization parameters for temporal alignment of video and accelerometer dataAnnotations as temporal intervals of pre- core- and post-phases of activities corresponding to steps in a recipeRelated publicationsSebastian Stein and Stephen J. McKenna, "User-adaptive models for recognizing food preparation activities", ACM International Conference on Multimedia (ACMMM 2013), 5th Workshop on Multimedia for Cooking and Eating Activities (CEA 2013), Barcelona, Spain, October 21. 2013. (PDF)Sebastian Stein and Stephen J. McKenna, "Combining Embedded Accelerometers with Computer Vision for Recognizing Food Preparation Activities" The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), Zurich, Switzerland, 2013.

Related Papers

  • Annotations as temporal intervals of pre- core- and post-phases of activities corresponding to steps in a recipe [link]
  • Synchronization parameters for temporal alignment of video and accelerometer data [link]
  • 3-axis accelerometer data at 50 Hz of devices attached to a knife, a mixing spoon, a small spoon, a peeler, a glass, an oil bottle, and a pepper dispenser. [link]
  • Depth maps 640x480 pixels at 30 Hz [link]
  • RGB video data 640x480 pixels at 30 Hz [link]

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