Title:
Investigating the Impact of Estimated Modalities in Multi-Modal Activity Recognition
Investigating the Impact of Estimated Modalities in Multi-Modal Activity Recognition
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Rajan, Rahul
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Abstract
RGB-D data obtained from affordable depth-sensors, like the XBox Kinect has allowed for remarkable progress in the field of human activity recognition (HAR). Depth information has been found to significantly increase performance in HAR tasks, especially when it’s fused with other modalities like RGB and Optical flow. Unfortu- nately, the use of depth sensors limits where these models can be used since these sensors are often difficult to use in outdoor settings. Additionally, most videos available today are shot on traditional video cameras, which don’t provide depth information needed to run RGB-D based HAR models. Fortunately, deep learning has al- lowed us to estimate this depth data with high accuracy from just RGB video. This paper investigates the viability of directly using this estimated depth information in RGB-D models for HAR-related tasks.
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2021-12
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Undergraduate Thesis