Motion measurements of explosive shock waves based on an event camera.

Shock wave measurement is vital in assessing explosive power and designing warheads. To obtain satisfactory observation data of explosive shock waves, it is preferable for optical sensors to possess high-dynamic range and high-time resolution capabilities. In this paper, the event camera is first employed to observe explosive shock waves, leveraging its high dynamic range and low latency. A comprehensive procedure is devised to measure the motion parameters of shock waves accurately. Firstly, the plane lines-based calibration method is proposed to compute the calibration parameters of the event camera, which utilizes the edge-sensitive characteristic of the event camera. Then, the fitted ellipse parameters of the shock wave are estimated based on the concise event data, which are gained by utilizing the characteristics of the event triggering and shock waves' morphology. Finally, the geometric relationship between the ellipse parameters and the radius of the shock wave is derived, and the motion parameters of the shock wave are estimated. To verify the performance of our method, we compare our measurement results in the TNT explosion test with the pressure sensor results and empirical formula prediction. The relative measurement error compared to pressure sensors is the lowest at 0.33% and the highest at 7.58%. The experimental results verify the rationality and effectiveness of our methods.

Event-based depth estimation with dense occlusion.

Occlusions pose a significant challenge to depth estimation in various fields, including automatic driving, remote sensing observation, and video surveillance. In this Letter, we propose a novel, to the best of our knowledge, depth estimation method for dense occlusion to estimate the depth behind occlusions. We design a comprehensive procedure using an event camera that consists of two steps: rough estimation and precise estimation. In the rough estimation, we reconstruct two segments of the event stream to remove occlusions and subsequently employ a binocular intersection measurement to estimate the rough depth. In the precise estimation, we propose a criterion that the maximum total length of edges of reconstructed images corresponds to the actual depth and search for the precise depth around the rough depth. The experimental results demonstrate that our method is implemented with relative errors of depth estimation below 1.05%.