Spike-Event Object Detection for Neuromorphic Vision

Neuromorphic vision is one of the novel research fields that study neuromorphic cameras and spiking neural networks (SNNs) for computer vision. Instead of computing on frame-based images, spike events are streamed from neuromorphic cameras, and novel object detection algorithms have to deal with spike events to achieve detection tasks. In this paper, we propose a solution to the novel object detection method with spike events. Spike events are first encoded to event images according to the computational methodology of neuromorphic theory. The event images can be realized as change-detected images of moving objects with a high frame rate. A redesigned deep learning framework is proposed for object detection to process the event images. We propose a deep SNN method that is achieved by the conversion of successful convolution neural networks but trained by event images. The networks with multiscale representation are discussed and designed in our method. We also design a semi-automatic data labeling method to build event-image datasets by object tracking algorithms. The proposed solution, therefore, includes spike event encoding, a redesigned deep SNN, and an event-image data augmentation algorithm. Experiments are conducted not only on the MNIST-DVS dataset, which is a benchmark dataset for the study of neuromorphic vision but also on our event pedestrian detection dataset. The experimental results show that the performance of the deep SNN trained with our augmented data is close to the model trained on manually labeled data. A performance comparison based on the PAFBenchmark dataset shows that our proposed method has higher accuracy than existing SNN methods, and better energy efficiency and lower energy consumption than existing CNN methods. It demonstrates that our deep SNN method is a feasible solution for the study of neuromorphic vision. The intuition that deep SNN trained with more learning data can achieve better accuracy is also confirmed for this brand-new research field.