Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
This provides a low-power solution for real-time motion estimation in embedded systems, though it is incremental as it adapts an existing method to a new hardware platform.
The paper tackles optical flow estimation from event-based vision sensors using a fully spike-based neural network implemented on IBM's TrueNorth, achieving an Average Endpoint Error of 11% with a power budget under 80mW.
This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data. A low power embedded implementation of the method which combines the Asynchronous Time-based Image Sensor with IBM's TrueNorth Neurosynaptic System is presented. The sensor generates spikes with sub-millisecond resolution in response to scene illumination changes. These spike are processed by a spiking neural network running on TrueNorth with a 1 millisecond resolution to accurately determine the order and time difference of spikes from neighboring pixels, and therefore infer the velocity. The spiking neural network is a variant of the Barlow Levick method for optical flow estimation. The system is evaluated on two recordings for which ground truth motion is available, and achieves an Average Endpoint Error of 11% at an estimated power budget of under 80mW for the sensor and computation.