Event-based attention and tracking on neuromorphic hardware
This work addresses the challenge of robust object tracking in dynamic environments for neuromorphic computing applications, representing an incremental improvement in event-based processing.
The authors tackled the problem of selective attention and tracking in event-based vision systems by implementing a fully event-driven system on neuromorphic hardware, achieving sustained activation for object tracking even with distractors or reduced event generation.
We present a fully event-driven vision and processing system for selective attention and tracking, realized on a neuromorphic processor Loihi interfaced to an event-based Dynamic Vision Sensor DAVIS. The attention mechanism is realized as a recurrent spiking neural network that implements attractor-dynamics of dynamic neural fields. We demonstrate capability of the system to create sustained activation that supports object tracking when distractors are present or when the object slows down or stops, reducing the number of generated events.