  Dynamic Vision Sensors (DVS), which output asynchronous log intensity change
events, have potential applications in high-speed robotics, autonomous cars and
drones. The precise event timing, sparse output, and wide dynamic range of the
events are well suited for optical flow, but conventional optical flow (OF)
algorithms are not well matched to the event stream data. This paper proposes
an event-driven OF algorithm called adaptive block-matching optical flow
(ABMOF). ABMOF uses time slices of accumulated DVS events. The time slices are
adaptively rotated based on the input events and OF results. Compared with
other methods such as gradient-based OF, ABMOF can efficiently be implemented
in compact logic circuits. Results show that ABMOF achieves comparable accuracy
to conventional standards such as Lucas-Kanade (LK). The main contributions of
our paper are new adaptive time-slice rotation methods that ensure the
generated slices have sufficient features for matching,including a feedback
mechanism that controls the generated slices to have average slice displacement
within the block search range. An LK method using our adapted slices is also
implemented. The ABMOF accuracy is compared with this LK method on natural
scene data including sparse and dense texture, high dynamic range, and fast
motion exceeding 30,000 pixels per second.The paper dataset and source code are
available from http://sensors.ini.uzh.ch/databases.html.

Computer Vision and Pattern Recognition