3D Data Acquisition Platform for Human Activity Understanding

Implementing motion capture devices, 3D vision sensors, and EMG sensors to cross validate multimodality data acquisition and address fundamental research problems involving the representation and invariant description of 3D data, human motion modeling and applications of human activity analysis, and computational optimization of large-scale 3D data.

Army Research Office, Research Triangle Park, North Carolina

Reliable online recognition and prediction of human actions and activities in temporal sequences has many potential applications in a wide range of Army-relevant fields, ranging from video surveillance, warfighter assistance, human computer interface, intelligent humanoid robots, and unmanned and autonomous vehicles, to diagnosis, assessment and treatment of musculoskeletal disorders, etc. A computational approach for action prediction can extend these findings to machines and also promote further research in human prediction and intention sensing.

Apparently, a practical prediction system must output a rapid response for partial observations. This brings up a new challenge to the computational models and motivates machine learning researchers to make more progress. Moreover, action prediction will need to model temporal structures and may raise an important advance for action recognition. The underlying basic goal is to enhance the DoD's capabilities of visual intelligence for leveraging automatic human activity understanding using 3D data acquisition platforms.