Two-stage Local Spatio-temporal Event Filter based on Adaptive Thresholds

Recently, the emerging event camera shows great potential for robotics and AR/VR application thanks to its advantages including low latency, high dynamic range, low power consumption, etc. However, the output of event cameras usually has a large amount of noise which will affect unlocking their potentials and obtaining wider applications. In this paper, we present a two-stage local spatio-temporal event filter (LSTEF) based on adaptive thresholds. Considering the spatio-temporal constraints of generated event streams, a local sliding window is adopted for the noise candidate selection stage and noise filtering stage. An adaptive thresholding mechanism is also introduced into the filter in order to improve the generalization performance. Corresponding experimental evaluations are performed on the public datasets to prove the efficiency of the proposed filter. Results show that the presented LSTEF can successfully achieve the event denoising, and at the same time, effectively preserve useful scene information.