Abstract:
The event camera is a relatively new type of sensor where each pixel asynchronously reports changes in incident light, resulting in low latency and high energy efficiency...Show MoreMetadata
Abstract:
The event camera is a relatively new type of sensor where each pixel asynchronously reports changes in incident light, resulting in low latency and high energy efficiency. This opens for new possibilities within counter Unmanned Aerial Vehicle (UAV) applications, crucial to tackle the threat of this increasingly widespread technology. However, this calls for new data processing methods. As a contribution to both of these challenges, we investigate the possibility of tracking UAVs that cover only a single or few pixels in event camera data. Scene and background activity noise is suppressed using a novel noise filter. Tracking is performed in the image plane using a standard Multi-Hypothesis Tracker (MHT) with parameters optimised for the data. The method is evaluated on several sequences of UAV data collected in an outdoor setting, exhibiting a variety of motion patterns and target sizes. The GOSPA measure and the fraction of observations within the covariance estimate are used to evaluate the results. Excellent tracking performance is achieved for regularly moving targets with a size of 2-15 pixels. Given the UAV and setup used in this study, this corresponds to distances of about 10-30 m when viewing the UAV from the side, or 20-65 m when viewed from below. Tracking is still possible for irregular motion in the same range, whereas rapid movements at close range and single-pixel targets are more challenging. We show that it is perfectly viable to track UAVs of various pixel sizes in event-based data using an MHT algorithm with an appropriate denoising method.
Date of Conference: 08-11 July 2024
Date Added to IEEE Xplore: 11 October 2024
ISBN Information: