Robust Tracking in Aerial Imagery Based on an Ego-Motion Bayesian Model

A novel strategy for object tracking in aerial imagery is presented, which is able to deal with complex situations where the camera ego-motion cannot be reliably estimated due to the aperture problem (related to low structured scenes), the strong ego-motion, and/or the presence of independent moving objects. The proposed algorithm is based on a complex modeling of the dynamic information, which simulates both the object and the camera dynamics to predict the putative object locations. In this model, the camera dynamics is probabilistically formulated as a weighted set of affine transformations that represent possible camera ego-motions. This dynamic model is used in a Particle Filter framework to distinguish the actual object location among the multiple candidates, that result from complex cluttered backgrounds, and the presence of several moving objects. The proposed strategy has been tested with the aerial FLIR AMCOM dataset, and its performance has been also compared with other tracking techniques to demonstrate its efficiency

Design and performance assessment of correlation filters for the detection of objects in high clutter thermal imagery

The research reported in this thesis has examined means of enhancing the performance of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter for target detection in Forward Looking Infra-Red (FLIR) imagery acquired from a helicopter and border security FLIR camera in northern Kuwait. The data acquired with these FLIR sensors allows real-world evaluation of the comparative performance of the various filters that have been developed in the thesis. The results obtained have been quantified using well known performance measures such as Peak to Side-lobe Ratio (PSR) and Total Detection Error (TDE). The initial focus was to study the effect of modifying the OT-MACH parameters on the correlation metrics. A new optimisation technique has been presented, which computes statistically the filter alpha parameter associated with controlling the response of the filter to clutter noise. A further modification of the OT-MACH filter performance using the Difference of Gaussian bandpass filter (named the D-MACH filter) as a pre-processing stage has been described. The D-MACH has been applied to several test images containing single and multiple targets in the scene. Enhanced performance of the modified filter is demonstrated with improved metrics being obtained with less false side peaks in the correlation plane, especially when multiple targets are present in the test images. A further pre-processing technique was investigated using the Rayleigh distribution as a pre-processing filter (named the R-MACH filter). The R-MACH filter has been applied to multiple target types with tests conducted across various image data sets. The filter demonstrated an improvement over the Difference of Gaussian filter in terms of 6 reducing the number of parameters needing to be tuned whilst producing further enhanced correlation plane metrics. Finally, recommendations for future work has been made to improve the use of the OT-MACH filter in target detection and identification. A novel training image representation is proposed for further investigation, which will minimise the computational intensity of using the MACH filter for unconstrained object recognition

Principal Component Analysis based Image Fusion Routine with Application to Stamping Split Detection

This dissertation presents a novel thermal and visible image fusion system with application in online automotive stamping split detection. The thermal vision system scans temperature maps of high reflective steel panels to locate abnormal temperature readings indicative of high local wrinkling pressure that causes metal splitting. The visible vision system offsets the blurring effect of thermal vision system caused by heat diffusion across the surface through conduction and heat losses to the surroundings through convection. The fusion of thermal and visible images combines two separate physical channels and provides more informative result image than the original ones. Principal Component Analysis (PCA) is employed for image fusion to transform original image to its eigenspace. By retaining the principal components with influencing eigenvalues, PCA keeps the key features in the original image and reduces noise level. Then a pixel level image fusion algorithm is developed to fuse images from the thermal and visible channels, enhance the result image from low level and increase the signal to noise ratio. Finally, an automatic split detection algorithm is designed and implemented to perform online objective automotive stamping split detection. The integrated PCA based image fusion system for stamping split detection is developed and tested on an automotive press line. It is also assessed by online thermal and visible acquisitions and illustrates performance and success. Different splits with variant shape, size and amount are detected under actual operating conditions

Machine vision applications in UAVs for autonomous aerial refueling and runway detection

This research focuses on the application of Machine Vision (MV) techniques and algorithms to the problems of Autonomous Aerial Refueling (AAR) and Runway Detection. In particular, real laboratory based hardware was used in a simulated environment to emulate real-life conditions for AAR. It was shown that the K-Means Clustering Algorithm solution to the Marker Detection problem could be executed at a frame rate of 30 Hz and it averaged a tracking error of less than one pixel while utilizing only 0.16% of the image. It was also shown that the solution to the Runway Detection problem could be executed at a frame rate of 20 Hz which is acceptable for use in an UAV performing reconnaissance work. Data from these tests suggest that both software schemes are suitable for applications in moving vehicles and that the accuracy of the measurements produced by the schemes make them suitable for UAV applications

Vision based strategies for implementing Sense and Avoid capabilities onboard Unmanned Aerial Systems

Current research activities are worked out to develop fully autonomous unmanned platform systems, provided with Sense and Avoid technologies in order to achieve the access to the National Airspace System (NAS), flying with manned airplanes. The TECVOl project is set in this framework, aiming at developing an autonomous prototypal Unmanned Aerial Vehicle which performs Detect Sense and Avoid functionalities, by means of an integrated sensors package, composed by a pulsed radar and four electro-optical cameras, two visible and two Infra-Red. This project is carried out by the Italian Aerospace Research Center in collaboration with the Department of Aerospace Engineering of the University of Naples “Federico II”, which has been involved in the developing of the Obstacle Detection and IDentification system. Thus, this thesis concerns the image processing technique customized for the Sense and Avoid applications in the TECVOL project, where the EO system has an auxiliary role to radar, which is the main sensor. In particular, the panchromatic camera performs the aiding function of object detection, in order to increase accuracy and data rate performance of radar system. Therefore, the thesis describes the implemented steps to evaluate the most suitable panchromatic camera image processing technique for our applications, the test strategies adopted to study its performance and the analysis conducted to optimize it in terms of false alarms, missed detections and detection range. Finally, results from the tests will be explained, and they will demonstrate that the Electro-Optical sensor is beneficial to the overall Detect Sense and Avoid system; in fact it is able to improve upon it, in terms of object detection and tracking performance

Development and implementation of image fusion algorithms based on wavelets

Image fusion is a process of blending the complementary as well as the common features of a set of images, to generate a resultant image with superior information content in terms of subjective as well as objective analysis point of view. The objective of this research work is to develop some novel image fusion algorithms and their applications in various fields such as crack detection, multi spectra sensor image fusion, medical image fusion and edge detection of multi-focus images etc. The first part of this research work deals with a novel crack detection technique based on Non-Destructive Testing (NDT) for cracks in walls suppressing the diversity and complexity of wall images. It follows different edge tracking algorithms such as Hyperbolic Tangent (HBT) filtering and canny edge detection algorithm. The second part of this research work deals with a novel edge detection approach for multi-focused images by means of complex wavelets based image fusion. An illumination invariant hyperbolic tangent filter (HBT) is applied followed by an adaptive thresholding to get the real edges. The shift invariance and directionally selective diagonal filtering as well as the ease of implementation of Dual-Tree Complex Wavelet Transform (DT-CWT) ensure robust sub band fusion. It helps in avoiding the ringing artefacts that are more pronounced in Discrete Wavelet Transform (DWT). The fusion using DT-CWT also solves the problem of low contrast and blocking effects. In the third part, an improved DT-CWT based image fusion technique has been developed to compose a resultant image with better perceptual as well as quantitative image quality indices. A bilateral sharpness based weighting scheme has been implemented for the high frequency coefficients taking both gradient and its phase coherence in accoun

Advanced machine learning approaches for target detection, tracking and recognition

This dissertation addresses the key technical components of an Automatic Target Recognition (ATR) system namely: target detection, tracking, learning and recognition. Novel solutions are proposed for each component of the ATR system based on several new advances in the field of computer vision and machine learning. Firstly, we introduce a simple and elegant feature, RelCom, and a boosted feature selection method to achieve a very low computational complexity target detector. Secondly, we present a particle filter based target tracking algorithm that uses a quad histogram based appearance model along with online feature selection. Further, we improve the tracking performance by means of online appearance learning where appearance learning is cast as an Adaptive Kalman filtering (AKF) problem which we formulate using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Then, we introduce an integrated tracking and recognition system that uses two generative models to accommodate the pose variations and maneuverability of different ground targets. Specifically, a tensor-based generative model is used for multi-view target representation that can synthesize unseen poses, and can be trained from a small set of signatures. In addition, a target-dependent kinematic model is invoked to characterize the target dynamics. Both generative models are integrated in a graphical framework for joint estimation of the target's kinematics, pose, and discrete valued identity. Finally, for target recognition we advocate the concept of a continuous identity manifold that captures both inter-class and intra-class shape variability among training targets. A hemispherical view manifold is used for modeling the view-dependent appearance. In addition to being able to deal with arbitrary view variations, this model can determine the target identity at both class and sub-class levels, for targets not present in the training data. The proposed components of the ATR system enable us to perform low computational complexity target detection with low false alarm rates, robust tracking of targets under challenging circumstances and recognition of target identities at both class and sub-class levels. Experiments on real and simulated data confirm the performance of the proposed components with promising results

Application of Machine Vision in UAVs for Autonomous Target Tracking

This research presents experimental results for the application of Machine Vision (MV) techniques to address the problem of target detection and tracking. The main objective is the design of a prototype UAV surveillance environment to emulate real-life conditions. The model environment for this experiment consists of a target simulated by a small electric train system, located at ground level, and a MV camera mounted on a motion-based apparatus located directly above the model setup. This system is meant to be a non-flying mockup of an aerial robot retrofitted with a MV sensor. Therefore, the final design is a two degree-of-freedom gantry simulating aircraft motions above the ground level at a constant altitude. On the ground level, the design of the landscape is an attempt to achieve a realistic natural landscape within a laboratory setting. Therefore, the scenery consists of small scale trees, bushes, a mountain, and a tunnel system within a 914 mm by 1066 mm boundary. To detect and track the moving train, MV algorithms are implemented in a Matlab/SimulinkRTM based simulation environment. Specifically, image pre-processing techniques and circle detection algorithms are implemented to detect and identify the chimney stack on the train engine. The circle detection algorithms analyzed in this research effort consists of a least squares based method and the Hough transform (HT) method for circle detection. The experimental results will show that the solution to the target detection problem could produce a positive detection rate of 90% during each simulation while utilizing only 56% of the input image. Tracking and timing data also shows that the least squares based target detection method performs substantially better then the HT method. This is evident from the result of using a 1--2 Hz frequency update rate for the SimulinkRTM scheme which is acceptable, in some cases, for use in navigation for a UAV performing scouting and reconnaissance missions. The development of vision-based control strategies, similar to the approach presented in this research, allows UAVs to participate in complex missions involving autonomous target tracking