Memristive Motion-Streak Neuron for Spatiotemporal Multiple Object Detection

Conventional artificial vision systems process dynamic scenes inefficiently by reconstructing motion from discrete frames, a process that requires post-processing. In contrast, real-world environments containing multiple moving objects demand sensor-level discrimination. This work presents a memristive motion-streak neuron that performs spatiotemporal encoding by integrating an Al/InGaZnO/Al optomemristor with an Ag/HfO2/Pt dynamic memristor, whose relaxation dynamics provide temporal memory. In this system, the presence time of moving objects is detected by decay of the output current, allowing motion direction and speed to be directly inferred from the relaxation behavior. The integrated memristor pixel array enables processing of continuous movements and achieves 96.2% classification accuracy for multiple objects. Also, integrating the motion-streak neuron with the resistor–capacitor kernel further encodes temporal intervals between optical events, enabling recognition of complex movement patterns. This event-driven processing diminishes computational overhead and provides a hardware solution for next-generation vision systems.