Silicon retinas, also known as Dynamic Vision Sensors (DVS) or event-based visual sensors, have shown great advantages in terms of low power consumption, low bandwidth, wide dynamic range and very high temporal resolution. Owing to such advantages as compared to conventional vision sensors, DVS devices are gaining more and more attention in various applications such as drone surveillance, robotics, high-speed motion photography, etc. The output of such sensors is a sequence of events rather than a series of frames as for classical cameras. Estimating the data rate of the stream of events associated with such sensors is needed for the appropriate design of transmission systems involving such sensors. In this work, we propose to consider information about the scene content and sensor speed to support such estimation, and we identify suitable metrics to quantify the complexity of the scene for this purpose. According to the results of this study, the event rate shows an exponential relationship with the metric associated with the complexity of the scene and linear relationships with the speed of the sensor. Based on these results, we propose a two-parameter model for the dependency of the event rate on scene complexity and sensor speed. The model achieves a prediction accuracy of approximately 88.4% for the outdoor environment along with the overall prediction performance of approximately 84%.
Frame Synchronization of High-Speed Vision Sensors with Respect to Temporally Encoded Illumination in Highly Dynamic Environments