An algorithm for tracking single visual targets is developed in this study. Feature detection is the necessary and appropriate image processing technique for this algorithm. The main point of this approach is to use the data supplied by the feature detection as the observation from a group of targets having similar motion dynamics. Therefore a single visual target is regarded as a group of multiple targets. Accurate data association and state estimation under clutter are desired for this application similar to other multi-target tracking applications. The group tracking approach is used with the well-known probabilistic data association technique to cope with data association and estimation problems. The applicability of this method particularly for visual tracking and for other cases is also discussed.

Data association problem for multitarget tracking is determination of the relationship between targets and the incoming measurements from sensors of the target tracking system. Performance of a multitarget tracking system is strongly related to the chosen method for data association and target tracking algorithm. Incorrect data association effects state estimation of targets. In this thesis, we propose a new multi-scan data association algorithm for multitarget tracking systems. This algorithm was implemented by using MATLAB programming tool. Performances of the new algorithm and JPDA method for multiple targets tracking are compared. During simulations linear models are used and the uncertainties in the sensor and motion models are modeled by Gaussian density. Simulation results are presented. Results show that the new algorithm&#039 / s performance is better than that of JPDA method. Moreover, a survey over target tracking literature is presented including basics of multitarget tracking systems and existing data association methods.

Visual surveillance of human crowds in a dynamic environment has attracted a great amount of computer vision research efforts in recent years. Moving object detection, which conventionally includes motion segmentation and optionally, object classification, is the first major task for any visual surveillance application. After detecting the targets, estimation of their geo-locations is needed to create the same reference coordinate system for them for higher-level decision-making. Depending on the required fidelity of decision, multi-target data association may be also needed at higher levels to differentiate multiple targets in a series of frames. Applying all these vision-based algorithms to a crowd surveillance system (a major application studied in this dissertation) using a team of cooperative unmanned vehicles (UVs), introduces new challenges to the problem. Since the visual sensors move with the UVs, and thus the targets and the environment are dynamic, it adds to the complexity and uncertainty of the video processing. Moreover, the limited onboard computation resources require more efficient algorithms to be proposed. Responding to these challenges, the goal of this dissertation is to design and develop an effective and efficient visual surveillance system based on dynamic data driven application system (DDDAS) paradigm to be used by the cooperative UVs for autonomous crowd control and border patrol. The proposed visual surveillance system includes different modules: 1) a motion detection module, in which a new method for detecting multiple moving objects, based on sliding window is proposed to segment the moving foreground using the moving camera onboard the unmanned aerial vehicle (UAV); 2) a target recognition module, in which a customized method based on histogram-of-oriented-gradients is applied to classify the human targets using the onboard camera of unmanned ground vehicle (UGV); 3) a target geo-localization module, in which a new moving-landmark-based method is proposed for estimating the geo-location of the detected crowd from the UAV, while a heuristic method based on triangulation is applied for geo-locating the detected individuals via the UGV; and 4) a multi-target data association module, in which the affinity score is dynamically adjusted to comply with the changing dispersion of the detected targets over successive frames. In this dissertation, a cooperative team of one UAV and multiple UGVs with onboard visual sensors is used to take advantage of the complementary characteristics (e.g. different fidelities and view perspectives) of these UVs for crowd surveillance. The DDDAS paradigm is also applied toward these vision-based modules, where the computational and instrumentation aspects of the application system are unified for more accurate or efficient analysis according to the scenario. To illustrate and demonstrate the proposed visual surveillance system, aerial and ground video sequences from the UVs, as well as simulation models are developed, and experiments are conducted using them. The experimental results on both developed videos and literature datasets reveal the effectiveness and efficiency of the proposed modules and their promising performance in the considered crowd surveillance application.

In this thesis we explore an infrared light-based approach to the problem of pose estimation during aircraft landing in urban environments where GPS is unreliable or unavailable. We introduce a novel fiducial constellation composed of sparse infrared lights that incorporates projective invariant properties in its design to allow for robust recognition and association from arbitrary camera perspectives. We propose a pose estimation pipeline capable of producing high accuracy pose measurements at real-time rates from monocular infrared camera views of the fiducial constellation, and present as part of that pipeline a data association method that is able to robustly identify and associate individual constellation points in the presence of clutter and occlusions. We demonstrate the accuracy and efficiency of this pose estimation approach on real-world data obtained from multiple flight tests, and show that we can obtain decimeter level accuracy from distances of over 100 m from the constellation. To achieve greater robustness to the potentially large number of outlier infrared detections that can arise in urban environments, we also explore learning-based approaches to the outlier rejection and data association problems. By formulating the problem of camera image data association as a 2D point cloud analysis, we can apply deep learning methods designed for 3D point cloud segmentation to achieve robust, high-accuracy associations at constant real-time speeds on infrared images with high outlier-to-inlier ratios. We again demonstrate the efficiency of our learning-based approach on both synthetic and real-world data, and compare the results and limitations of this method to our first-principles-based data association approach.

Multitarget tracking in the presence of false alarm is a difficult problem to consider. The objective of multitarget tracking is to estimate the number of targets and their states recursively from available observations. At any given time, targets can be born, die and spawn from already existing targets. Sensors can detect these targets with a defined threshold, where normally the observation is influenced by false alarm. Also if the targets are with low signal to noise ratio (SNR) then the targets may not be detected. The Random Finite Set (RFS) filters can be used to solve such multitarget problem efficiently. Specially, one of the best and most widely used RFS based filter is the Probability Hypothesis Density (PHD) filter. The PHD filter approximates the posterior probability density function (PDF) by the first order moment only, where the targets SNR assumed to be much higher. The PHD filter supports targets die, born, spawn and missed-detection by using the well known implementations including Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) and Gaussian Mixture Probability Hypothesis Density (GM-PHD) methods. The SMC-PHD filter suffers from the well known degeneracy problems while GM-PHD filter may not be suitable for nonlinear and non-Gaussian target tracking problems. It is desirable to have a filter that can provide continuous estimates for any distribution. This is the motivation for the use of B-Splines in this thesis. One of the main focus of the thesis is the B-Spline based PHD (SPHD) filters. The Spline is a well developed theory and been used in academia and industry for more than five decades. The B-Spline can represent any numerical, geometrical and statistical functions and models including the PDF and PHD. The SPHD filter can be applied to linear, nonlinear, Gaussian and non-Gaussian multitarget tracking applications. The SPHD continuity can be maintained by selecting splines with order of three or more, which avoids the degeneracy-related problem. Another important characteristic of the SPHD filter is that the SPHD can be locally controlled, which allow the manipulations of the SPHD and its natural tendency for handling the nonlinear problems. The SPHD filter can be further extended to support maneuvering multitarget tracking, where it can be an alternative to any available PHD filter implementations. The PHD filter does not work well for very low observable (VLO) target tracking problems, where the targets SNR is normally very low. For very low SNR scenarios the PDF must be approximated by higher order moments. Therefore the PHD implementations may not be suitable for the problem considered in this thesis. One of the best estimator to use in VLO target tracking problem is the Maximum-Likelihood Probability Data Association (ML-PDA) algorithm. The standard ML-PDA algorithm is widely used in single target initialization or geolocation problems with high false alarm. The B-Spline is also used in the ML-PDA (SML-PDA) implementations. The SML-PDA algorithm has the capability to determine the global maximum of ML-PDA log-likelihood ratio with high efficiency in terms of state estimates and low computational complexity. For fast passive track initialization, search and rescue operations the SML-PDA algorithm can be used more efficiently compared to the standard ML-PDA algorithm. Also the SML-PDA algorithm with the extension supports the multitarget tracking. / Thesis / Doctor of Philosophy (PhD)

Today many car manufactures are developing systems that help the driver to avoid collisions. Examples of this kind of systems are: adaptive cruise control, collision warning and collision mitigation / avoidance. All these systems need to track and predict future positions of surrounding objects (vehicles ahead of the system host vehicle), to calculate the risk of a future collision. To validate that a prediction is correct the predictions must be correlated to observations. This is called the data association problem. If a prediction can be correlated to an observation, this observation is used for updating the tracking filter. This process maintains the low uncertainty level for the track. From the work behind this thesis, it has been found that a sequential nearest- neighbour approach for the solution of the problem to correlate an observation to a prediction can be used to find the solution to the data association problem. Since the computational power for the collision mitigation system is limited, only the most dangerous surrounding objects can be tracked and predicted. Therefore, an algorithm that classifies and selects the most critical measurements is developed. The classification into order of potential risk can be done using the measurements that come from an observed object.

<p>Today many car manufactures are developing systems that help the driver to avoid collisions. Examples of this kind of systems are: adaptive cruise control, collision warning and collision mitigation / avoidance. </p><p>All these systems need to track and predict future positions of surrounding objects (vehicles ahead of the system host vehicle), to calculate the risk of a future collision. To validate that a prediction is correct the predictions must be correlated to observations. This is called the data association problem. If a prediction can be correlated to an observation, this observation is used for updating the tracking filter. This process maintains the low uncertainty level for the track. </p><p>From the work behind this thesis, it has been found that a sequential nearest- neighbour approach for the solution of the problem to correlate an observation to a prediction can be used to find the solution to the data association problem. </p><p>Since the computational power for the collision mitigation system is limited, only the most dangerous surrounding objects can be tracked and predicted. Therefore, an algorithm that classifies and selects the most critical measurements is developed. The classification into order of potential risk can be done using the measurements that come from an observed object.</p>

ABSTRACT EVALUATION OF MULTI TARGET TRACKING ALGORITHMS IN THE PRESENCE OF CLUTTER G&uuml / ner, Onur M.S., Department of Electrical and Electronics Engineering Supervisor: Prof. Dr. Mustafa Kuzuoglu August 2005, 88 Pages This thesis describes the theoretical bases, implementation and testing of a multi target tracking approach in radar applications. The main concern in this thesis is the evaluation of the performance of tracking algorithms in the presence of false alarms due to clutter. Multi target tracking algorithms are composed of three main parts: track initiation, data association and estimation. Two methods are proposed for track initiation in this work. First one is the track score function followed by a threshold comparison and the second one is the 2/2 &amp / M/N method which is based on the number of detections. For data association problem, several algorithms are developed according to the environment and number of tracks that are of interest. The simplest method for data association is the nearest-neighbor data association technique. In addition, the methods that use multiple hypotheses like probabilistic data association and joint probabilistic data association are introduced and investigated. Moreover, in the observation to track assignment, gating is an important issue since it reduces the complexity of the computations. Generally, ellipsoidal gates are used for this purpose. For estimation, Kalman filters are used for state prediction and measurement update. In filtering, target kinematics is an important point for the modeling. Therefore, Kalman filters based on different target kinematic models are run in parallel and the outputs of filters are combined to yield a single solution. This method is developed for maneuvering targets and is called interactive multiple modeling (IMM). All these algorithms are integrated to form a multi target tracker that works in the presence (or absence) of clutter. Track score function, joint probabilistic data association (JPDAF) and interactive multiple model filtering are used for this purpose. Keywords: clutter, false alarms, track initiation, data association, gating, target kinematics, IMM, JPDAF

The effective application of mobile robotics requires that robots be able to perform tasks with an extended degree of autonomy. Simultaneous localisation and mapping (SLAM) aids automation by providing a robot with the means of exploring an unknown environment while being able to position itself within this environment. Vision-based SLAM benefits from the large amounts of data produced by cameras but requires intensive processing of these data to obtain useful information. In this dissertation it is proposed that, as the saliency content of an image distils a large amount of the information present, it can be used to benefit vision-based SLAM implementations. The proposal is investigated by developing a new landmark for use in SLAM. Image keypoints are grouped together according to the saliency content of an image to form the new landmark. A SLAM system utilising this new landmark is implemented in order to demonstrate the viability of using the landmark. The landmark extraction, data filtering and data association routines necessary to make use of the landmark are discussed in detail. A Microsoft Kinect is used to obtain video images as well as 3D information of a viewed scene. The system is evaluated using computer simulations and real-world datasets from indoor structured environments. The datasets used are both newly generated and freely available benchmarking ones. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

Source localization with a wireless sensor network remains an important area of research as the number of applications with this problem increases. This work considers the problem of source localization by a network of passive wireless sensors. The primary means by which localization is achieved is through direction-finding at each sensor, and in some cases, range estimation as well. Both single and multiple-target scenarios are considered in this research. In single-source environments, a solution that outperforms the classic least squared error estimation technique by combining direction and range estimates to perform localization is presented. In multiple-source environments, two solutions to the complex data association problem are addressed. The first proposed technique offers a less complex solution to the data association problem than a brute-force approach at the expense of some degradation in performance. For the second technique, the process of signal classification is considered as another approach to the data association problem. Environments in which each signal possesses unique features can be exploited to separate signals at each sensor by their characteristics, which mitigates the complexity of the data association problem and in many cases improves the accuracy of the localization. Two approaches to signal-selective localization are considered in this work. The first is based on the well-known cyclic MUSIC algorithm, and the second combines beamforming and modulation classification. Finally, the implementation of a direction-finding system is discussed. This system includes a uniform circular array as a radio frequency front end and the universal software radio peripheral as a data processor. / Master of Science