Simultaneous neuromorphic selection of multiple salient objects for event vision
Résumé
The combined use of spiking neural networks and event cameras is gaining momentum in the field of embedded computer vision as they promise to reduce latency and computational resource requests. However, state-of-the-art embedded neuromorphic models show little interest in modifying input data to optimise model performance, memory usage, latency, and power consumption. This work addresses this optimisation trade-off by implementing a neuromorphic model of salient selection, which simultaneously outputs multiple segregated objects of interest detected in an event-based scene. This work extends previous ones and identifies regions of interest as those corresponding to a high spatiotemporal density of events. Without any training and with a limited number of neurons, the proposed model is able to simultaneously detect different objects with a delay of only 14ms at most, and filtered objects maintain 73% of the original data’s classification performance. We are thus confident that the method proposed in this paper will allow for improving the subsequent neuromorphic processing of event data on embedded systems. To the best of our knowledge, it is the first neuromorphic model able to simultaneously select multiple objects of interest. Our code can be found here: github.com/amygruel/FoveationStakes_DVS/.
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