Motion Estimation for Spike Camera Data Sequence via Spike Interval Analysis

With the development of emerging computer vision applications, there is an increasing demand for capturing the scenes with high-speed motion. Recently, a novel retina-inspired spike camera has shown great potential for recording the dynamic scenes at high temporal resolution. Different from the conventional digital cameras that capture the visual scene by a single snapshot, the spike camera monitors the incoming light persistently, with each pixel producing a continuous stream of spikes. Recovering the motion process from the spike data sequence is an important problem to study for the spike camera, as it is the foundation of many other tasks, such as image reconstruction, object tracking and object detection. In this paper, we carefully analyze the characteristics of spike data and develop a motion estimation algorithm to recover the continuous high-speed motion process from the spike camera data sequence. Based on the assumption that the spike intervals passed by the same motion trajectories usually have the similar spike densities, we establish a data term constraint to model the temporal consistency of spike intervals. In addition, we integrate a local smoothness constraint with the proposed data term constraint to further improve the estimation accuracy. Experimental results demonstrate that our proposed algorithm can recover high-speed motion process from the captured spike data, and the recovered motion information is beneficial for i mage reconstruction.