Abstract
Newly emerged dynamic vision sensors (DVS) offer a great potential over traditional sensors (e.g. CMOS) since they have a high temporal resolution in the order of \(\mu s\), ultra-low power consumption and high dynamic range up to 140 dB compared to 60 dB in frame cameras. Unlike traditional cameras, the output of DVS cameras is a stream of events that encodes the location of the pixel, time, and polarity of the brightness change. An event is triggered when the change of brightness, i.e. log intensity, of a pixel exceeds a certain threshold. The output of event cameras often contains a significant amount of noise (outlier events) alongside the signal (inlier events). The main cause of that is transistor switch leakage and noise. This paper presents a dynamic background activity filtering, called DBA-filter, for event cameras based on an adaptation of the K-nearest neighbor (KNN) algorithm and the optical flow. Results show that the proposed algorithm is able to achieve a high signal to noise ratio up to 13.64 dB.
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This work was supported by the Academy of Finland under the project (314048).
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Mohamed, S.A.S., Yasin, J.N., Haghbayan, MH., Heikkonen, J., Tenhunen, H., Plosila, J. (2022). DBA-Filter: A Dynamic Background Activity Noise Filtering Algorithm for Event Cameras. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_44
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