Tracking moving targets in complex environments by fusing active and passive sensors

摘要

We present a novel algorithm for tracking with ladar sensors to aid in navigation, guidance and control systems, suitable for applications to unmanned air vehicles. The methods we employ are based on Bayesian segmentation, optical flow, active contour and Bayesian particle tracking. The algorithm herein holds several significant advantages over traditional tracking methods. The first step in the process is the optimal segmentation of images to enhance the targets and extract them from background clutter and noise. The Bayesian approach to segmentation allows the use of intensity (passive) and range (active) imagery to find targets. Optical flow generalizes and improves correlation techniques for locating objects within a frame, allowing for aspect angle and range changes. With optical flow, we may infer relative velocities on a pixel-by-pixel basis. Active contours are ideally suited to both target-sparse and target-rich environments. The energy approach to determining contours allows the merging and separating of potential targets in an automatic manner. Bayesian particle tracking techniques are used to track the contours over time. The algorithm is tested successfully on experimental and simulated ladar data (using both intensity and range data) as well as sequences of video imageries. The streamlined processing, from obtaining the image data (of size 805x148 pixels) to detecting the moving target to wrapping an active contour on the target, takes less than one second of clock time and provides very accurate predictions of the target location in future frames.