Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras
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Event-based cameras are bioinspired sensors able to perceive changes in the scene at high frequency with a low power consumption. Becoming available only very recently, a limited amount of work addresses object detection on these devices. In this paper we propose two neural networks architectures for object detection: YOLE, which integrates the events into frames and uses a frame-based model to process them, eFCN, a event-based fully convolutional network that uses a novel and general formalization of the convolutional and max pooling layers to exploit the sparsity of the camera events. We evaluated the algorithm with different extension of publicly available dataset, and on a novel custom dataset.
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