  In this paper, we introduce a novel method to capture visual trajectories for
navigating an indoor robot in dynamic settings using streaming image data.
First, an image processing pipeline is proposed to accurately segment
trajectories from noisy backgrounds. Next, the captured trajectories are used
to design, train, and compare two neural network architectures for predicting
acceleration and steering commands for a line following robot over a continuous
space in real time. Lastly, experimental results demonstrate the performance of
the neural networks versus human teleoperation of the robot and the viability
of the system in environments with occlusions and/or low-light conditions.
