  We explore the possibility of using a single monocular camera to forecast the
time to collision between a suitcase-shaped robot being pushed by its user and
other nearby pedestrians. We develop a purely image-based deep learning
approach that directly estimates the time to collision without the need of
relying on explicit geometric depth estimates or velocity information to
predict future collisions. While previous work has focused on detecting
immediate collision in the context of navigating Unmanned Aerial Vehicles, the
detection was limited to a binary variable (i.e., collision or no collision).
We propose a more fine-grained approach to collision forecasting by predicting
the exact time to collision in terms of milliseconds, which is more helpful for
collision avoidance in the context of dynamic path planning. To evaluate our
method, we have collected a novel dataset of over 13,000 indoor video segments
each showing a trajectory of at least one person ending in a close proximity (a
near collision) with the camera mounted on a mobile suitcase-shaped platform.
Using this dataset, we do extensive experimentation on different temporal
windows as input using an exhaustive list of state-of-the-art convolutional
neural networks (CNNs). Our results show that our proposed multi-stream CNN is
the best model for predicting time to near-collision. The average prediction
error of our time to near collision is 0.75 seconds across the test videos.
