Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020
The lack of large-scale, labeled data sets impedes progress in developing robust and generalized ... more The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR). Labeled data in human activity recognition is scarce and hard to come by, as sensor data collection is expensive, and the annotation is time-consuming and error-prone. To address this problem, we introduce IMUTube, an automated processing pipeline that integrates existing computer vision and signal processing techniques to convert videos of human activity into virtual streams of IMU data. These virtual IMU streams represent accelerometry at a wide variety of locations on the human body. We show how the virtually-generated IMU data improves the performance of a variety of models on known HAR datasets. Our initial results are very promising, but the greater promise of this work lies in a collective approach by the computer vision, signal processing, and activity recognition communities to extend this work in ways ...
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and quantization. In this paper, we present a data-driven approach for refining degraded RAW depth maps that are
coupled with an RGB image. The key idea of our approach is to take advantage of a training set of high-quality depth
data and transfer its information to the RAW depth map through multi-scale dictionary learning. Utilizing a sparse
representation, our method learns a dictionary of geometric primitives which captures the correlation between high-
quality mesh data, RAW depth maps and RGB images. The dictionary is learned and applied in a manner that accounts
for various practical issues that arise in dictionary-based depth refinement. Compared to previous approaches that only
utilize the correlation between RAW depth maps and RGB images, our method produces improved depth maps without
over-smoothing. Since our approach is data driven, the refinement can be targeted to a specific class of objects by
employing a corresponding training set. In our experiments, we show that this leads to additional improvements in
recovering depth maps of human faces.
and quantization. In this paper, we present a data-driven approach for refining degraded RAW depth maps that are
coupled with an RGB image. The key idea of our approach is to take advantage of a training set of high-quality depth
data and transfer its information to the RAW depth map through multi-scale dictionary learning. Utilizing a sparse
representation, our method learns a dictionary of geometric primitives which captures the correlation between high-
quality mesh data, RAW depth maps and RGB images. The dictionary is learned and applied in a manner that accounts
for various practical issues that arise in dictionary-based depth refinement. Compared to previous approaches that only
utilize the correlation between RAW depth maps and RGB images, our method produces improved depth maps without
over-smoothing. Since our approach is data driven, the refinement can be targeted to a specific class of objects by
employing a corresponding training set. In our experiments, we show that this leads to additional improvements in
recovering depth maps of human faces.