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

ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors