  Estimating a scene's depth to achieve collision avoidance against moving
pedestrians is a crucial and fundamental problem in the robotic field. This
paper proposes a novel, low complexity network architecture for fast and
accurate human depth estimation and segmentation in indoor environments, aiming
to applications for resource-constrained platforms (including battery-powered
aerial, micro-aerial, and ground vehicles) with a monocular camera being the
primary perception module. Following the encoder-decoder structure, the
proposed framework consists of two branches, one for depth prediction and
another for semantic segmentation. Moreover, network structure optimization is
employed to improve its forward inference speed. Exhaustive experiments on
three self-generated datasets prove our pipeline's capability to execute in
real-time, achieving higher frame rates than contemporary state-of-the-art
frameworks (114.6 frames per second on an NVIDIA Jetson Nano GPU with TensorRT)
while maintaining comparable accuracy.
