abstract: The tracking of multiple targets becomes more challenging in complex environments due to the additional degrees of nonlinearity in the measurement model. In urban terrain, for example, there are multiple reflection path measurements that need to be exploited since line-of-sight observations are not always available. Multiple target tracking in urban terrain environments is traditionally implemented using sequential Monte Carlo filtering algorithms and data association techniques. However, data association techniques can be computationally intensive and require very strict conditions for efficient performance. This thesis investigates the probability hypothesis density (PHD) method for tracking multiple targets in urban environments. The PHD is based on the theory of random finite sets and it is implemented using the particle filter. Unlike data association methods, it can be used to estimate the number of targets as well as their corresponding tracks. A modified maximum-likelihood version of the PHD (MPHD) is proposed to automatically and adaptively estimate the measurement types available at each time step. Specifically, the MPHD allows measurement-to-nonlinearity associations such that the best matched measurement can be used at each time step, resulting in improved radar coverage and scene visibility. Numerical simulations demonstrate the effectiveness of the MPHD in improving tracking performance, both for tracking multiple targets and targets in clutter. / Dissertation/Thesis / M.S. Electrical Engineering 2011

International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, Nevada / Military aerospace test ranges are increasingly being called upon to conduct missions utilizing large numbers of participating units, or targets. Precision, position and trajectory data must be recorded on all participants. In addition, weapon/target engagements must be scored and real-time range safety considerations must be accommodated. This requires precision metric data be available in real-time on all participating targets. One solution to these problems, is utilization of multiple target tracking radars which incorporate electronic beam steering to quickly move from one target to another in sequence. This paper briefly recounts the history of range instrumentation radars, points out some of the advantages of using multi-target radars, and highlights the specifications and design of a multiple target instrumentation radar now being acquired by the U.S. Army for use at White Sands Missile Range and the Kwajalein Missile Range.

Reliable track continuity is an important characteristic of multiple target tracking (MTT) algorithms. In the specific case of visually tracking multiple ground targets from an aerial platform, challenges arise due to realistic operating environments such as video compression artifacts, unmodeled camera vibration, and general imperfections in the target detection algorithm. Some popular visual detection techniques include Kanade-Lucas-Tomasi (KLT)-based motion detection, difference imaging, and object feature matching. Each of these algorithmic detectors has fundamental limitations in regard to providing consistent measurements. In this thesis we present a scalable detection framework that simultaneously leverages multiple measurement sources. We present the recursive random sample consensus (R-RANSAC) algorithm in a data fusion architecture that accommodates multiple measurement sources. Robust track continuity and real-time performance are demonstrated with post-processed flight data and a hardware demonstration in which the aircraft performs automated target following. Applications involving autonomous tracking of ground targets occasionally encounter situations where semantic information about targets would improve performance. This thesis also presents an autonomous target labeling framework that leverages cloud-based image classification services to classify targets that are tracked by the R-RANSAC MTT algorithm. The communication is managed by a Python robot operating system (ROS) node that accounts for latency and filters the results over time. This thesis articulates the feasibility of this approach and suggests hardware improvements that would yield reliable results. Finally, this thesis presents a framework for image-based target recognition to address the problem of tracking targets that become occluded for extended periods of time. This is done by collecting descriptors of targets tracked by R-RANSAC. Before new tracks are assigned an ID, an attempt to match visual information with historical tracks is triggered. The concept is demonstrated in a simulation environment with a single target, using template-based target descriptors. This contribution provides a framework for improving track reliability when faced with target occlusions.

<p>This thesis considers the problem of tracking multiple static or moving targets with one single pan/tilt-camera with a limited field of view. The objective is to minimize both the time needed to pan and tilt the camera's view between the targets and the total position uncertainty of all targets. To solve this problem, several planning methods have been developed and evaluated by Monte Carlo simulations and real world experiments. If the targets are moving and their true positions are unknown, both their current and future positions need to be estimated in order to calculate the best sensor trajectory. When dealing with static and known targets the problem is reduced to a deterministic optimization problem.</p><p>The planners have been tested through experiments using a real camera mounted above a car track using toy cars as targets. An algorithm has been developed to detect the cars and associate the detections with the correct target.</p><p>The Monte Carlo simulations show that, in the case of static targets, there is a huge advantage to arrange the targets into groups to be able to view more than one target at the time. In the case of moving targets with estimated positions it can be concluded that if the objective is to minimize the error in the position estimation the best planning choice is to always move to the target with the highest position uncertainty.</p>

No description available.

This thesis considers the problem of tracking multiple static or moving targets with one single pan/tilt-camera with a limited field of view. The objective is to minimize both the time needed to pan and tilt the camera's view between the targets and the total position uncertainty of all targets. To solve this problem, several planning methods have been developed and evaluated by Monte Carlo simulations and real world experiments. If the targets are moving and their true positions are unknown, both their current and future positions need to be estimated in order to calculate the best sensor trajectory. When dealing with static and known targets the problem is reduced to a deterministic optimization problem. The planners have been tested through experiments using a real camera mounted above a car track using toy cars as targets. An algorithm has been developed to detect the cars and associate the detections with the correct target. The Monte Carlo simulations show that, in the case of static targets, there is a huge advantage to arrange the targets into groups to be able to view more than one target at the time. In the case of moving targets with estimated positions it can be concluded that if the objective is to minimize the error in the position estimation the best planning choice is to always move to the target with the highest position uncertainty.

Understanding the content of a video sequence is not a particularly difficult problem for humans. We can easily identify objects, such as people, and track their position and pose within the 3D world. A computer system that could understand the world through videos would be extremely beneficial in applications such as surveillance, robotics, biology. Despite significant advances in areas like tracking and, more recently, 3D static scene understanding, such a vision system does not yet exist. In this work, I present progress on this problem, restricted to videos of objects that move in smoothly and which are relatively easily detected, such as people. Our goal is to identify all the moving objects in the scene and track their physical state (e.g., their 3D position or pose) in the world throughout the video. We develop a Bayesian generative model of a temporal scene, where we separately model data association, the 3D scene and imaging system, and the likelihood function. Under this model, the video data is the result of capturing the scene with the imaging system, and noisily detecting video features. This formulation is very general, and can be used to model a wide variety of scenarios, including videos of people walking, and time-lapse images of pollen tubes growing in vitro. Importantly, we model the scene in world coordinates and units, as opposed to pixels, allowing us to reason about the world in a natural way, e.g., explaining occlusion and perspective distortion. We use Gaussian processes to model motion, and propose that it is a general and effective way to characterize smooth, but otherwise arbitrary, trajectories. We perform inference using MCMC sampling, where we fit our model of the temporal scene to data extracted from the videos. We address the problem of variable dimensionality by estimating data association and integrating out all scene variables. Our experiments show our approach is competitive, producing results which are comparable to state-of-the-art methods.

abstract: Neural activity tracking using electroencephalography (EEG) and magnetoencephalography (MEG) brain scanning methods has been widely used in the field of neuroscience to provide insight into the nervous system. However, the tracking accuracy depends on the presence of artifacts in the EEG/MEG recordings. Artifacts include any signals that do not originate from neural activity, including physiological artifacts such as eye movement and non-physiological activity caused by the environment. This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique time-frequency features are first extracted using matching pursuit decomposition for both neural activity and artifact signals. The features are used to model probability density functions for each signal type using Gaussian mixture modeling for use in the PPHDF neural tracking algorithm. The probability density function of the artifacts provides information to the tracking algorithm that can help reduce the probability of incorrectly estimating the dynamically varying number of current dipole sources and their corresponding neural activity localization parameters. Simulation results demonstrate the effectiveness of the proposed algorithm in increasing the tracking accuracy performance for multiple dipole sources using recordings that have been contaminated by artifacts. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2014

Multi-target tracking (MTT) methods estimate the trajectory of targets from noisy measurement; therefore, they can be used to handle the pedestrian-vehicle interaction for a moving vehicle. MTT has an important part in assisting the Automated Driving System and the Advanced Driving Assistance System to avoid pedestrian-vehicle collisions. ADAS and ADS rely on correct estimates of the pedestrians' position and velocity, to avoid collisions or unnecessary emergency breaking of the vehicle. Therefore, to help the risk evaluation in these systems, the MTT needs to provide accurate and robust information of the trajectories (in terms of position and velocity) of the pedestrians in different environments. Several factors can make this problem difficult to handle for instance in crowded environments the pedestrians can suffer from occlusion or missed detection. Classical MTT methods, such as the global nearest neighbour filter, can in crowded environments fail to provide robust and accurate estimates. Therefore, more sophisticated MTT methods should be used to increase the accuracy and robustness and, in general, to improve the tracking of targets close to each other. The aim of this master's thesis is to improve the situational awareness with respect to pedestrians and pedestrian-vehicle interactions. In particular, the task is to investigate if the GM-PHD and the GM-CPHD filter improve pedestrian tracking in urban environments, compared to other methods presented in the literature.  The proposed task can be divided into three parts that deal with different issues. The first part regards the significance of different clustering methods and how the pedestrians are grouped together. The implemented algorithms are the distance partitioning algorithm and the Gaussian mean shift clustering algorithm. The second part regards how modifications of the measurement noise levels and the survival of targets based on the target location, with respect to the vehicle's position, can improve the tracking performance and remove unwanted estimates. Finally, the last part regards the impact the filter estimates have on the tracking performance and how important accurate detections of the pedestrians are to improve the overall tracking. From the result the distance partitioning algorithm is the favourable algorithm, since it does not split larger groups. It is also seen that the proposed filters provide correct estimates of pedestrians in events of occlusion or missed detections but suffer from false estimates close to the ego vehicle due to uncertain detections. For the comparison, regarding the improvements, a classic standard MTT filter applying the global nearest neighbour method for the data association is used as the baseline. To conclude; the GM-CPHD filter proved to be the best out of the two proposed filters in this thesis work and performed better also compared to other methods known in the literature. In particular, its estimates survived for a longer period of time in presence of missed detection or occlusion. The conclusion of this thesis work is that the GM-CPHD filter improves the tracking performance and the situational awareness of the pedestrians.

In this thesis, the Recursive-Random Sample Consensus (R-RANSAC) multiple target tracking (MTT) algorithm is further developed and applied to video taken from static platforms. Development of R-RANSAC is primarily focused in three areas: data association, the ability to track maneuvering objects, and track management. The probabilistic data association (PDA) filter performs very well in the R-RANSAC framework and adds minimal computation cost over less sophisticated methods. The interacting multiple models (IMM) filter as well as higher-order linear models are incorporated into R-RANSAC to improve tracking of highly maneuverable targets. An effective track labeling system, a more intuitive track merging criteria, and other improvements were made to the track management system of R-RANSAC. R-RANSAC is shown to be a modular algorithm capable of incorporating the best features of competing MTT algorithms. A comprehensive comparison with the Gaussian mixture probability hypothesis density (GM-PHD) filter was conducted using pseudo-aerial videos of vehicles and pedestrians. R-RANSAC maintains superior track continuity, especially in cases of interacting and occluded targets, and has fewer missed detections when compared with the GM-PHD filter. The two algorithms perform similarly in terms of the number of false positives and tracking precision. The concept of a feedback loop between the tracker and sensor processing modules is extensively explored; the output tracks from R-RANSAC are used to inform how video processing is performed. We are able to indefinitely detect stationary objects by zeroing out the background update rate of target-associated pixels in a Gaussian mixture models (GMM) foreground detector. False positive foreground detections are eliminated with a minimum blob area threshold, a ghost suppression algorithm, and judicious tuning of the R-RANSAC parameters. The ability to detect stationary targets also allows R-RANSAC to be applied to a class of problems known as stationary object detection. Additionally, moving camera foreground detection techniques are applied to the static camera case in order to produce measurements with a velocity component; this is accomplished by using sequential-RANSAC to cluster optical flow vectors of FAST feature pairs. This further improves R-RANSAC's track continuity, especially with interacting targets. Finally, a hybrid algorithm composed of R-RANSAC and the Sequence Model (SM), a machine learner, is presented. The SM learns sequences of target locations and is able to assist in data association once properly trained. In simulation, we demonstrate the SM's ability to significantly improve tracking performance in situations with infrequent measurement updates and a high proportion of clutter measurements.