mmWave radar
We present a comprehensive multi-sensor dataset comprising 4D mmWave radar point clouds, lidar point clouds, and camera images in the open-pit mine. The dataset encompasses various operational scenarios, including the dumping site, loading site, connecting roads, and haulage maintenance area, under various lighting conditions such as cloudy, dark, daylight, and overcast skies.
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We introduce a novel multi-modal dataset comprising point cloud data from a mmWave radar, RGB and depth images from an RGB-D camera, collected from 23 human subjects.
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Human activity recognition, which involves recognizing human activities from sensor data, has drawn a lot of interest from researchers and practitioners as a result of the advent of smart homes, smart cities, and smart systems. Existing studies on activity recognition mostly concentrate on coarse-grained activities like walking and jumping, while fine-grained activities like eating and drinking are understudied because it is more difficult to recognize fine-grained activities than coarse-grained ones.
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