A subpixel-resolution image registration algorithm based on the nonlinear projective transformation model is proposed to account for camera translation, rotation, zoom, pan, and tilt. Typically, parameter estimation techniques for transformation models require the user to manually select feature points between the images undergoing registration. In this research, block matching is used to automatically select correlated feature point pairs between two images, and these features are used to calculate an iterative least squares solution for the projective transformation parameters. Since block matching is capable of estimating accurate translation motion vectors only in discontinuous edge regions, inaccurate feature point pairs are statistically eliminated prior to computing the least squares parameter estimate. Convergence of the projective transformation model estimation algorithm is generally achieved in several iterations. After subpixel-resolution image registration, a high-resolution video still may be computed by integrating the registered pixels from a short sequence of low-resolution image sequence frames.

A number of technologies are being developed for Concealed Weapon Detection (CWD). Use of appropriate processing techniques will be very important to the success of such technologies. This article describes digital image processing procedures currently being investigated to enhance the detection of weapons concealed underneath clothing.

Modern surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets. In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow in distributed fusion systems and for developing algorithms. Fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusion architectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given.