This PDF file contains the front matter associated with SPIE Proceedings Volume 7338, including the Title Page, Copyright information, Tabe of Contents, Introduction (if any), and the Conference Committee listing.

The objective of this research is to develop control methods to attenuate laser beam jitter using a fast-steering mirror. Adaptive filter controllers using Filtered-X least mean square and Filtered-X recursive least square algorithms are explored. The disturbances that cause beam jitter include mechanical vibrations on the optical platform (narrowband) and atmospheric turbulence (broadband). Both feedforward filters (with the use of auxiliary reference sensor(s)) and feedback filters (with only output feedback) are investigated. Hybrid adaptive filters, which are a combination of feedback and feedforward, are also examined. For situations when obtaining a coherent feedforward reference signal is not possible, methods for incorporating multiple semi-coherent reference signals into the control law are developed. The controllers are tested on a jitter control testbed to prove their functionality. The testbed is equipped with shakers mounted to the optical platform and a disturbance fast-steering mirror to simulate the effects of atmospheric propagation. Experimental results showed that the feedback adaptive filter controller was superior to the feedforward technique, and the hybrid method achieved the best overall results.

Conventional mirror line-of-sight stabilization approaches, such as the heliostat and coleostat, are configured such that the sensor input line-of-sight is always oriented parallel to a gimbal axis. While provisions must be made to accommodate the inherent two-to-one mirror kinematics in these approaches, the resulting angular rates orthogonal to the line-of-sight are linear functions of the angular rates of the gimbals and of the base to which the sensor and gimbals are attached, and they are un-coupled such that the two orthogonal line-of-sight axes can be controlled independently. If, however, the sensor and gimbal cannot be oriented such that the sensor input line-of-sight is parallel to one of the gimbal axes, the line-of-sight angular rate kinematics become non-linear and coupled. The purpose of this paper is to present the development of the angular rate kinematic equations for such a system. The angular rate equations which result are coupled and non-linear but account for the line-of-sight motion caused by both the angular motion of the gimbals and the angular motion of the base and thus can be used to inertially stabilize and point the line-of-sight about two orthogonal axes using measurements from the two gimbal angle transducers and three orthogonal gyros.

Gimbaled planar mirrors are used to point and stabilize a camera's or laser's line-ofsight (LOS). The mirror with its reflection property adds another degree of complexity to the already complex area of LOS pointing and stabilization modeling and control. For example, when the optics and detector are located off gimbal and utilizing a 2-axis gimbaled mirror to point the LOS, the image at the detector rolls one for one with the outer gimbal rotation. This is difficult to understand unless the equations are developed to show this. The LOS pointing kinematic equations for a planar gimbaled mirror begin with the mirror reflectance equation [2,3]. This equation describes the reflected ray or vector in terms of the incoming ray and the mirror unit normal, and ultimately creates the mathematical relationship between the reflected ray, the base (or a user-defined reference frame), and the detector reference frame. This kinematic relationship is differentiated to form the LOS rate equations from which one can easily see what states are necessary, and how these states are combined, for inertially stabilizing the LOS about its roll, pitch, and yaw axes.

Deformable bimorph mirrors with high damage threshold dielectric coatings are demonstrated as both intra- and extracavity components with a pulsed energy diode-pumped Nd:YAG zigzag slab laser operating at the 150 mJ level. Two resonator configurations were tested for intra-cavity operation; with a plane-plane resonator the far-field brightness was enhanced by up to a factor of 1.5, whilst with a cross-Porro resonator an enhancement of up to 2.2 was achieved. As an extracavity component far-field beam steering of ±2 mrad was demonstrated.

Conventional air-to-ground target acquisition processes treat the image stream in isolation from external data sources. This ignores information that may be available through modern mission management systems which could be fused into the detection process in order to provide enhanced performance. By way of an example relating to target detection, this paper explores the use of a-priori knowledge and other sensor information in an adaptive architecture with the aim of enhancing performance in decision making. The approach taken here is to use knowledge of target size, terrain elevation, sensor geometry, solar geometry and atmospheric conditions to characterise the expected spatial and radiometric characteristics of a target in terms of probability density functions. An important consideration in the construction of the target probability density functions are the known errors in the a-priori knowledge. Potential targets are identified in the imagery and their spatial and expected radiometric characteristics are used to compute the target likelihood. The adaptive architecture is evaluated alongside a conventional non-adaptive algorithm using synthetic imagery representative of an air-to-ground target acquisition scenario. Lastly, future enhancements to the adaptive scheme are discussed as well as strategies for managing poor quality or absent a-priori information.

An adaptive image pre-processor has been developed as a high-performance front-end for a next-generation multi-target tracking (MTT) system. The tracking system is designed to track targets across potentially multiple and distributed electro-optic video sensors. Typically a pre-processor operates to enhance targets and assist the tracking. However, they frequently rely on expert knowledge to configure the algorithm for the particular application and hence do not cope adequately given unexpected variations or generic application. The pre-processor developed for our MTT system achieves a significantly improved and robust performance by using an adaptive approach based on wavelet decomposition and a "supporting-classifier" method. It is capable of detecting and dynamically maintaining a target-definition optimized for tracking, whilst maximally suppressing non-related clutter. This paper presents an overview of the architecture and demonstrates its performance on real video scenes.

Shadows constitute a problem in many moving object detection and tracking algorithms in video. Usually, moving shadow regions lead to larger regions for detected objects. Shadow pixels have almost the same chromaticity as the original background pixels but they only have lower brightness values. Shadow regions usually retain the underlying texture, surface pattern, and color value. Therefore, a shadow pixel can be represented as a.x where x is the actual background color vector in 3-D RGB color space and a is a positive real number less than 1. In this paper, a shadow detection method based on two-dimensional (2-D) cepstrum is proposed.

One of the important tasks in video surveillance is to detect and track targets moving independently in a scene. Most real-time research to date has focused on scenarios from stationary cameras where there is limited movement in the background, such as videos taken at traffic lights or from buildings where there is no background proximal to the background. A more robust method is needed when there are moving background objects such as trees or flags close in the camera or when the camera is moving. In this paper we first introduce a variant of the multimodal mean (MM) background model that we call the spatial multimodal mean (SMM) background model that is better suited for these scenarios while improving the speed of the mixture of Gaussians (MoG) background model. It approximates the multimodal MoG background with the generalization that each pixel has a random spatial distribution. The SMM background model is well suited for real-time nonstationary scenes since it models each pixel with a spatial distribution and the simplifications make it computationally feasible to apply image transformations. We then describe how this can be integrated into a real-time MTI system that does not require the estimation of depth.

This paper solves four problems associated with typical correlation tracking systems. The first problem is that uncertainty in the position observation of an object is not propagated from the detection stage to the tracking stage. The second problem is that the shape of the reference template always lags the actual shape of the object. The third problem is the need for a separate acquisition process to generate the initial reference template. The fourth problem is the inability to track multiple objects. To overcome these problems we developed the Shape Estimating Filter (SEF), a homogeneous extension of the basic correlation tracker; and its multi-target counterpart the Competitive Attentional Correlation Tracker using Shape (CACTuS).

Advanced image registration technique with sub-pixel accuracy has been developed and applied for TD (time-differencing) process [1]. The TD process can help to suppress heavy background clutter for improved moving target detection. After processing a CFAR (constant false alarm rate) thresholding detector on the time-differenced image frames, we have developed and applied an image domain moving target tracking (IDMTT) process for robust moving target tracking. The IDMTT process uses a unique location feature by mapping and associating the real moving targets in the previous time-differenced frame with the ghost moving targets in the current time-differenced frame. The accurate location mapping and associating information between time frames is provided by the registration process. Preliminary tests for the IDMTT process are promising. Robust moving target tracking can be achieved even under quite low signal-to-clutter noise-ratio (SCNR = 0.5).

A method for detecting an object's motion in images that suffer from camera shake or images with camera egomotion is proposed. This approach is based on edge orientation codes and on the entropy calculated from a histogram of the edge orientation codes. Here, entropy is extended to spatio-temporal entropy. We consider that the spatio-temporal entropy calculated from time-series orientation codes can represent motion complexity, e.g., the motion of a pedestrian. Our method can reject false positives caused by camera shake or background motion. Before the motion filtering, object candidates are detected by a frame-subtraction-based method. After the filtering, over-detected candidates are evaluated using the spatio-temporal entropy, and false positives are then rejected by a threshold. This method could reject 79 to 96 [%] of all false positives in road roller and escalator scenes. The motion filtering decreased the detection rate somewhat because of motion coherency or small apparent motion of a target. In such cases, we need to introduce a tracking method such as Particle Filter or Mean Shift Tracker. The running speed of our method is 32 to 46 ms per frame with a 160×120 pixel image on an Intel Pentium 4 CPU at 2.8 GHz. We think that this is fast enough for real-time detection. In addition, our method can be used as pre-processing for classifiers based on support vector machines or Boosting.

A Liquid Crystal Tunable Filter (LCTF) camera could form part of an inexpensive system for color-aided target tracking; however, the standard tracking techniques will need to be adapted to the cyclic color information where only one wavelength is measured each timestep. A bayesian multi-hypothesis tracking algorithm is well adapted for this scenario, as it allows for track association decisions to be delayed until complete spectra are gathered. The design and tuning of bayesian multi-hypothesis tracker will be described and its behavior demonstrated for a scenario in which a fixed-mounted LCTF camera is used in vehicle tracking.

In radar tracking, the Preferred Ordering Theorem for updating the state vector in rectangular coordinates using an Extended Kalman Filter states that the measurement components of a detection should be used sequentially in the order azimuth first, then elevation, and range last. Such is counterintuitive to a common belief that the most accurate measurement should be used first, since that is usually range. However, it is observed here that the theorem can lose its efficacy as a track converges - and that the expected value of an EKF update is not always well-defined. An "extension" is therefore given, which is dubbed the DKF after Desargues, since it is based on an analysis involving projective geometry. With this approach the conditional update of a track in rectangular coordinates becomes well defined in the sense that a preferred order is obviated, and it can now be updated using a range or angle observation, separately or sequentially in either order, with less error. In this presentation the basic issues are illustrated, and the DFK is defined and contrasted with the EKF for the two-dimensional motionless target case.

The multiple hypotheses tracker (mht) is recognized as an optimal tracking method due to the enumeration of all possible measurement-to-track associations, which does not involve any approximation in its original formulation. However, its practical implementation is limited by the NP-hard nature of this enumeration. As a result, a number of maintenance techniques such as pruning and merging have been proposed to bound the computational complexity. It is possible to improve the performance of a tracker, mht or not, using feature information (e.g., signal strength, size, type) in addition to kinematic data. However, in most tracking systems, the extraction of features from the raw sensor data is typically independent of the subsequent association and filtering stages. In this paper, a new approach, called the Judicious Multi Hypotheses Tracker (jmht), whereby there is an interaction between feature extraction and the mht, is presented. The measure of the quality of feature extraction is input into measurement-to-track association while the prediction step feeds back the parameters to be used in the next round of feature extraction. The motivation for this forward and backward interaction between feature extraction and tracking is to improve the performance in both steps. This approach allows for a more rational partitioning of the feature space and removes unlikely features from the assignment problem. Simulation results demonstrate the benefits of the proposed approach.

An air-to-air missile is always submitted to extremes conditions of temperature, such as a hot runway in the desert dropping down to very cold conditions at high altitudes. It is evident that the optical system must be able to provide satisfactory image quality under any circumstances without causing any major degradation to the image. Under this perspective, two different designs of optical systems will be considered for this missile: one catadioptric, using a modified Cassegrain telescope and another one purely dioptric. Both optical systems must be able to focus energy in two different arrays of detectors, one for the near infrared radiation and the other one for the medium infrared. Due to the special missile flight profile, the temperature operational range will be determined and considered in order to design and athermalize the optical systems. Due to the large temperatures range, the missile optical system will experience deformation effects that will cause defocus and image degradation. A correct choice of materials, including the telescope body and dome shroud must be determined to minimize the defocus effect. Also a thermal compensator ought to be strategically placed on both designs to provide focus correction for all the temperatures range. Following that, the optical designs will be analyzed for effects of stray light and ghost image to find out what are the most suitable absorbing paint and anti-reflective coatings to be used. In the last step, both systems will be classified accordingly to their characteristics of performance, weight, size, viability and price and the best will integrate the missile optical system.

Optical systems may contain mechanical structure, optics, sensors, and active control to improve image quality or to point and stabilize the line-of-sight. A single model that includes structural, optical, and active control elements is beneficial for trade studies and defining hardware requirements. The process and benefits of representing structural and optical elements as a state space model are discussed. A state space model is derived for a reaction-less steering mirror. Steering mirror control and performance are discussed. A method for creating state space models directly from finite element normal modes is also described. A single closed loop model that represents both structural and optical effects in the state space form can be used to quickly evaluate system performance.

This paper provides comparative evaluations of two visual object tracking algorithms - the Shape Estimating Filter (SEF), a homogeneous extension of the basic correlation tracker; and its multi-object counterpart the Competitive Attentional Correlation Tracker using Shape (CACTuS). The CACTuS is evaluated comparatively against its predecessor to show direct improvement in tracking effectiveness. Our approach will involve an evaluation framework consisting of a range of modern, peer reviewed tracking performance metrics, allowing for a detailed multi-faceted analysis of tracking results. As such we provide an overview of current performance evaluation methods, including techniques for multi-object tracker evaluation.

Time-differencing process, with the help of image registration techniques, is useful for image-domain moving target detection under heavy clutter conditions. Time-differencing between two well-registered image frames can significantly suppress the heavy static background clutter, and thus improve moving target detection. However, we may still lose detection of a moving target from time to time under heavy clutter conditions (Pd < 100%), and also we may lose detection of a moving target when this target stops moving. For example, a moving vehicle will temporarily stop moving in front of a red light or a stop sign. In general, the performance of a conventional tracking process depends on the performance of the detection process. In this paper, we present our newly developed image-domain moving target tracking and process using an adaptive local target correlation tracker. Once we started to track a target, the correlation tracker can continue to track this target, no matter whether we can still detect this target in the future image frames or not. Both single and multiple target tracking capabilities using the correlation tracker have been developed. Furthermore, while continuing to track a moving vehicle, we apply a super-resolution image enhancement (SRIE) process developed at SAIC [1] to improve the vehicle resolution and signal-to-noise ratio (SNR) for better automatic target recognition (ATR) or human/pilot-monitored recognition performance.