Infrared Stationary Object Acquisition and Moving Object Tracking

摘要

Currently, there is much interest in developing electro-optic and infrared stationary and moving object acquisition and tracking algorithms for Intelligence, Surveillance, and Reconnaissance (ISR) and other applications. Many of the existing EO/IR object acquisition and tracking techniques work well for good-quality images, when object parameters such as size are well-known. However, when dealing with noisy and distorted imagery many techniques are unable to acquire stationary objects nor acquire and track moving objects.rnThis paper will discuss two inter-related problems: (1) stationary object detection and segmentation and (2) moving object acquisition and tracking in a sequence of images that are acquired via an IR sensor mounted on both stationary and moving platforms.rn1. A stationary object detection and segmentation algorithm called "Weighted Adaptive Iterative Statistical Threshold (WAIST)" will be described. The WAIST algorithm takes any intensity image and separates object pixels from the background or clutter pixels. Two common image processing techniques are nearest neighbors clustering and statistical thresholding. The WAIST algorithm uses both techniques iteratively, making best use of both techniques. Statistical threshold takes advantage of the fact that object pixels will exist above a threshold based on the statistical properties of the known noise pixels in the image. The nearest neighbor technique takes advantage of the fact that when many neighboring pixels are known object pixels, the pixel in question is more likely to be a object pixel. The WAIST algorithm initializes the nearest neighbor parameters and statistical threshold parameters and adjusts them iteratively to converge to an optimal solution. Each iteration of the algorithm conservatively declares a pixel to be noise as the statistical threshold is raised. This algorithm has proven to segment objects of interest from noisy backgrounds and clutter. Results of the effort are presented.rn2. For moving object detection and tracking we identify the challenges that the user faces in this problem; in particular, blind geo-registration of the acquired spatially-warped imagery and their calibration. For moving object acquisition and tracking we present an adaptive signal/image processing approach that utilizes multiple frames of the acquired imagery for geo-registration and sensor calibration. Our method utilizes a cost function to associate detected moving objects in adjacent frames and these results are used to identify the motion track of each moving object in the imaging scene. Results are presented using a ground-based panning IR camera.