  Due to its light and weather-independent sensing, millimeter-wave (MMW) radar
is essential in smart environments. Intelligent vehicle systems and
industry-grade MMW radars have integrated such capabilities. Industry-grade MMW
radars are expensive and hard to get for community-purpose smart environment
applications. However, commercially available MMW radars have hidden
underpinning challenges that need to be investigated for tasks like recognizing
objects and activities, real-time person tracking, object localization, etc.
Image and video data are straightforward to gather, understand, and annotate
for such jobs. Image and video data are light and weather-dependent,
susceptible to the occlusion effect, and present privacy problems. To eliminate
dependence and ensure privacy, commercial MMW radars should be tested. MMW
radar's practicality and performance in varied operating settings must be
addressed before promoting it. To address the problems, we collected a dataset
using Texas Instruments' Automotive mmWave Radar (AWR2944) and reported the
best experimental settings for object recognition performance using different
deep learning algorithms. Our extensive data gathering technique allows us to
systematically explore and identify object identification task problems under
cross-ambience conditions. We investigated several solutions and published
detailed experimental data.
