The Johns Hopkins University multimodal dataset for human action recognition

Thomas S. Murray; Daniel R. Mendat; Philippe O. Pouliquen; Andreas G. Andreou

doi:10.1117/12.2189349

21 May 2015 The Johns Hopkins University multimodal dataset for human action recognition

Thomas S. Murray, Daniel R. Mendat, Philippe O. Pouliquen, Andreas G. Andreou

Proceedings Volume 9461, Radar Sensor Technology XIX; and Active and Passive Signatures VI; 94611U (2015) https://doi.org/10.1117/12.2189349
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

The Johns Hopkins University MultiModal Action (JHUMMA) dataset contains a set of twenty-one actions recorded with four sensor systems in three different modalities. The data was collected with a data acquisition system that includes three independent active sonar devices at three different frequencies and a Microsoft Kinect sensor that provides both RGB and Depth data. We have developed algorithms for human action recognition from active acoustics and provide benchmark baseline recognition performance results.

Citation Download Citation

Thomas S. Murray, Daniel R. Mendat, Philippe O. Pouliquen, and Andreas G. Andreou "The Johns Hopkins University multimodal dataset for human action recognition", Proc. SPIE 9461, Radar Sensor Technology XIX; and Active and Passive Signatures VI, 94611U (21 May 2015); https://doi.org/10.1117/12.2189349

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