Queue-based Spatiotemporal Filter and Clustering for Dynamic Vision Sensor

Le résumé fourni par la source

Dynamic vision sensors (DVS) have significant potential in scenes involving high-speed motion and extreme light. However, DVS is sensitive to background active noise, which will degrade the quality of the output. The ordinary$O(N^{2})$-Space spatiotemporal filter's memory complexity is high. It needs$N\times N$memory cells ($N\times N$is the resolution on the sensor). Some works reduce memory complexity by sacrificing the performance of the filter. To ensure the filtering effect and reduce the filter's memory complexity, this paper proposes a novel filter: Queue-based spatiotemporal filter. Moreover, based on the Queue-based spatiotemporal filter, this paper proposes a clustering algorithm that can cluster while filtering. Experiments show that the proposed filter's performance is similar to the$O(N^{2})$-Space spatiotemporal filter while having a lower memory complexity. Besides, using the proposed clustering algorithm, the objects in motion can be clustered with low calculation complexity.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.