A Comparative Analysis of Kernel-Based Target Tracking Methods using Different Colour Feature Based Target Models

An effective target modeling is the root of a robust and efficient tracking system. Color feature is widely used feature space for target modeling in real time tracking applications because of its computational efficiency and invariance towards change in shape, scale and rotation. The effective use of this feature with kernel-based target tracking can lead to a robust tracking system. This paper provides a comparative analysis of the performance of three variants of kernel-based tracking system using color feature. The simulation results show that the target modeling using transformed background weighted target model will perform efficiently when initialized target has similar color feature with background while the combination of color-texture will be more accurate and robust when texture features are prominently present

Scaling Up Deliberative Democracy as Dispute Resolution in Healthcare Reform: A Work in Progress

Simultaneous Localisation and Mapping (SLAM) denotes the problem of jointly localizing a moving platform and mapping the environment. This work studies the SLAM problem using a combination of inertial sensors, measuring the platform's accelerations and angular velocities, and a monocular camera observing the environment. We formulate the SLAM problem on a nonlinear least squares (NLS) batch form, whose solution provides a smoothed estimate of the motion and map. The NLS problem is highly nonconvex in practice, so a good initial estimate is required. We propose a multi-stage iterative procedure, that utilises the fact that the SLAM problem is linear if the platform's rotations are known. The map is initialised with camera feature detections only, by utilising feature tracking and clustering of  feature tracks. In this way, loop closures are automatically detected. The initialization method and subsequent NLS refinement is demonstrated on both simulated and real data

Boosted Ringlet Features for Visual Object Tracking

Accurate and efficient object tracking is an important aspect in security and surveillance applications. Many challenges exist in visual object tracking including scale change, object distortion, lighting change, and occlusion. The combination of structural target information including edge features with the intensity or color features will allow for more robust object tracking in these conditions. To achieve this, we propose a feature extraction method that utilizes both the Frei-Chen edge detector and Gaussian ringlet feature mapping. Frei-Chen edge detector extracts edge, line, and mean features that can be combined to create an edge detection image. The edge detection image will then be used to represent the structural features of the target. Gaussian ringlet feature mapping is used to obtain rotational invariant features that are robust to target and viewpoint rotation. These aspects are combined to create an efficient and robust tracking scheme. The proposed method also includes occlusion and scale handling components. The proposed scheme is evaluated against state-of-the-art feature tracking methods using both temporal and spatial robustness metrics on the Visual Object Tracking 2014 database.https://ecommons.udayton.edu/stander_posters/2024/thumbnail.jp

Cardiovascular Magnetic Resonance Deformation Imaging By Feature Tracking For Assessment Of Left And Right Ventricular Structure And Function

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorCardiac magnetic resonance (CMR) imaging is the gold standard imaging technique for assessment of ventricular dimensions and function. CMR also allows assessment of ventricular deformation but this requires additional imaging sequences and time consuming post processing which has limited its widespread use. A novel CMR analysis software package, ‘feature tracking’ (Tom Tec, Germany) can measure ventricular deformation directly from cine CMR images. This thesis seeks to further our understanding of the feasibility of feature tracking to assess myocardial deformation and volumetric measures. Chapter 3 validates normal ranges for deformation parameters and compares values against traditional tagging measures. The work identifies global circumferential strain measures as being the most reproducible. In chapters 4 and 5, feature tracking values for left and right ventricular strain are compared with echocardiography derived speckle tracking indices of deformation. For left ventricular (LV) parameters, circumferential and longitudinal strain are most consistent and for the right ventricular (RV) measures, assessment of free wall strain using feature tracking shows promise and with modifications in algorithms is likely to further improve in the future. Chapter 6 assesses the ability of feature tracking to measure diastolic function. The results show that radial diastolic velocities and longitudinal diastolic strain rates can predict diastolic dysfunction (as diagnosed by echocardiography) with acceptable levels of sensitivity and specificity, particularly when used in combination. 11 The use of feature tracking to provide automated measures of ventricular volumes, mass and ejection fraction is assessed in chapter 7. Feature tracking in this context shows acceptable correlation but poor absolute agreement with manual contouring and further adjustments to algorithms is necessary to improve its accuracy. This work offers insights into the use of feature tracking for the assessment of ventricular deformation parameters. It is a technique with advantages over CMR tagging methods and given the speed of post processing has the potential to become the CMR preferred assessment for strain quantification in the future.I am indebted to the Engineering and Physical Sciences Research Council, the British Heart Foundation and the National Institute for Health Research Oxford Biomedical Research Centre for funding this work

Robust Multiple Object Tracking Using ReID features and Graph Convolutional Networks

Deep Learning allows for great advancements in computer vision research and development. An area that is garnering attention is single object tracking and multi-object tracking. Object tracking continues to progress vastly in terms of detection and building re-identification features, but more effort needs to be dedicated to data association. In this thesis, the goal is to use a graph neural network to combine the information from both the bounding box interaction as well as the appearance feature information in a single association chain. This work is designed to explore the usage of graph neural networks and their message passing abilities during tracking to come up with stronger data associations. This thesis combines all steps from detection through association using state of the art methods along with novel re-identification applications. The metrics used to determine success are Multi-Object Tracking Accuracy (MOTA), Multi-Object Tracking Precision (MOTP), ID Switching (IDs), Mostly Tracked, and Mostly Lost. Within this work, the combination of multiple appearance feature vectors to create a stronger single feature vector is explored to improve accuracy. Different types of data augmentations such as random erase and random noise are explored and their results are examined for effectiveness during tracking. A unique application of triplet loss is also implemented to improve overall network performance as well. Throughout testing, baseline models have been improved upon and each successive improvement is added to the final model output. Each of the improvements results in the sacrifice of some performance metrics but the overall benefits outweigh the costs. The datasets used during this thesis are the UAVDT Benchmark and the MOT Challenge Dataset. These datasets cover aerial-based vehicle tracking and pedestrian tracking. The UAVDT Benchmark and MOT Challenge dataset feature crowded scenery as well as substantial object overlap. This thesis demonstrates the increased matching capabilities of a graph network when paired with a robust and accurate object detector as well as an improved set of appearance feature vectors

Robust visual tracking via speedup multiple kernel ridge regression

Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods