The effect of noise filters on DVS event streams : Examining background activity filters on neuromorphic event streams
Abstract: Image classification using data from neuromorphic vision sensors is a challenging task that affects the use of dynamic vision sensor cameras in real- world environments. One impeding factor is noise in the neuromorphic event stream, which is often generated by the dynamic vision sensors themselves. This means that effective noise filtration is key to successful use of event- based data streams in real-world applications. In this paper we harness two feature representations of neuromorphic vision data in order to apply conventional frame-based image tools on the neuromorphic event stream. We use a standard noise filter to evaluate the effectiveness of noise filtration using a popular dataset converted to neuromorphic vision data. The two feature representations are the best-of-class standard Histograms of Averaged Time Surfaces (HATS) and a simpler grid matrix representation. To evaluate the effectiveness of the noise filter, we compare classification accuracies using various noise filter windows at different noise levels by adding additional artificially generated Gaussian noise to the dataset. Our performance metrics are reported as classification accuracy. Our results show that the classification accuracy using frames generated with HATS is not significantly improved by a noise filter. However, the classification accuracy of the frames generated with the more traditional grid representation is improved. These results can be refined and tuned for other datasets and may eventually contribute to on- the- fly noise reduction in neuromorphic vision sensors.
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