  Target detection is pivotal for modern urban computing applications. While
image-based techniques are widely adopted, they falter under challenging
environmental conditions such as adverse weather, poor lighting, and occlusion.
To improve the target detection performance under complex real-world scenarios,
this paper proposes an intelligent integrated optical camera and
millimeter-wave (mmWave) radar system. Utilizing both physical knowledge and
data-driven methods, a long-term robust radar-camera fusion algorithm is
proposed to solve the heterogeneous data fusion problem for detection
improvement. For the occlusion scenarios, the proposed algorithm can
effectively detect occluded targets with the help of memory through performing
long-term detection. For dark scenarios with low-light conditions, the proposed
algorithm can effectively mark the target in the dark picture as well as
provide rough stickman imaging. The above two innovative functions of the
hybrid optical camera and mmWave radar system are tested in real-world
scenarios. The results demonstrate the robustness and significant enhancement
in the target detection performance of our integrated system.
