In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map.
In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation.
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios.
In this paper, we take a comprehensive look into the effects of replacing real data with synthetic data.
In this paper, we present a multi-scale Fully Convolutional Networks (MSP-RFCN) to robustly detect and classify human hands under various challenging conditions.
We present a real-time action recognition system which integrates fast random sampling method with local spatio-temporal features extracted from a Local Part Model.