Danny Gibbins

Detecting suspicious background changes in video surveillance of busy scenes

Detecting background changes in scenes containing significant numbers of moving objects has several applications in video surveillance. One important example is the detection of suspicious packages left in busy airport terminals or train stations. The paper outlines a statistical approach to automatically detecting long term changes to the stationary component of a scene, and describes a prototype system which has been used to successfully demonstrate the feasibility of this approach.

Use of 3D ship scatterer models from ISAR image sequences for target recognition

Abstract Traditionally, inverse synthetic aperture radar (ISAR) image frames are classified individually in an automatic target recognition system. When information from different image frames is combined, it is usually in the context of time-averaging to remove statistically independent noise fluctuations between images. The sea state induced variability of the ship target projections between frames, however, also provides additional information about the target, which can be used to construct a 3D representation of the target scatterer positions. In this paper, a method for classifying a ship based on 3D scatterer information from a sequence of 2D ISAR images is described. A preliminary classification result for simulated ISAR images of nine types of ship is also provided.

A video geo-location and image enhancement tool for small unmanned air vehicles (UAVs)

This paper describes recent work conducted in collaboration with DSTO Australia and Aerosonde to develop video-based geolocation (targeting) and real-time enhancement of ground features observed from a small unmanned air vehicle (UAV). Here a combination of low-cost GPS, video and attitude sensors are used to estimate the object ground position whilst a simple super-resolution technique is used to enhance images of the object of interest. The results of two recent trials of the developed algorithms are presented.

Model based ISAR ship classification

Abstract This paper describes a method for ISAR image classification, based on a comparison of range-Doppler imagery to supplied 3D ship reference models. This comparison is performed in the image domain by first estimating ship motion for each frame in a sequence of ISAR images. ISAR images are then simulated using this estimated motion applied to some known 3D reference models, and the model image and real images are compared to produce a match score for use in classification. The effect of estimation error in each of the motion parameters is also investigated.

Multi-scale Feature Extraction for 3D Models Using Local Surface Curvature

In this paper, we present a method for extracting salient local features from 3D models using surface curvature which has application to 3D object recognition. In the developed technique, the amount of curvature at a point is specified by a positive number known as the curvedness. This value is invariant to rotation as well as translation. A local description of the surface is generated by fitting a surface to the neighbourhood of a keypoint and estimating its curvedness at multiple scales. From this surface, points corresponding to local maxima and minima of curvedness are selected as suitable features and a confidence measure of each keypoint is also calculated based on the deviation of its curvedness from the neighbouring values. Experimental results on a different number of models are shown to demonstrate the effectiveness and robustness of our approach.

Ship motion estimation from ISAR data

The motion of ships seen through a high resolution coherent radar is of interest in object recognition based on Doppler imaging. This paper describes a preliminary investigation into the estimation of ship motion based on an assumption of sinusoidal motion.

Features for high resolution radar range profile based ship classification

This study investigates a variety of features in the context of automated ship classification of high resolution radar range profile. The features used are length, scatterer count, centres of mass, quantised range profile and Fourier modified direct Mellin transform coefficients. The results of evaluation using a modest database of high resolution range profiles, collected using an airborne radar, are then presented.

Classifying ships using low resolution maritime radar

The classification of shipping using commercially available low resolution incoherent radars has useful applications in both harbour surveillance and naval defence. However, one major drawback is the relatively low resolution of the radar images. We present a brief overview of a real-time classification system currently under development, and present results of applying various classification schemes to low resolution radar images, in conjunction with track data, to classify surface craft into broad categories.

Multi-scale feature extraction for 3d surface registration using local shape variation

This paper describes a method for extracting salient local features from 3D models using shape variation which has application to 3D surface registration. In the proposed technique, the surface shape at a point is specified by a quantitative measure known as the shape index. It is invariant to rigid transformations such as translation and rotation. The shape index at a point is calculated at multiple scales by fitting a surface to the local neighbourhoods of different sizes. The local surface variation is then measured by calculating the variation of the shape index of every point in the neighbourhood. Points corresponding to local maxima of surface variation are selected as suitable features. Experimental results of applying the proposed feature extraction method on a variety of 3D models are shown to evaluate the effectiveness and robustness of our approach.

An empirical evaluation of infrared clutter for point target detection algorithms

This paper describes a study into the impact of local environmental conditions on the detection of point targets using wide field of view infrared sensors on airborne platforms. A survey of the common complexity metrics for measuring IR clutter, and common point target detection algorithms was conducted. A quantitative evaluation was performed using 20 hours of infrared imagery collected over a three month period from helicopter flights in a variety of clutter environments. The research method, samples of the IR data sets, and results of the correlation between environmental conditions, scene complexity metrics and point target detection algorithms are presented. The key findings of this work are that variations in IR detection performance can be attributed to a combination of environmental factors (but no single factor is sufficient to describe performance variations), and that historical clutter metrics are insufficient to describe the performance of modern detection algorithms.