Confidence-Level-Based New Adaptive Particle Filter for Nonlinear Object Tracking Regular Paper

Nonlinear object tracking from noisy measurements is a basic skill and a challenging task of mobile robotics, especially under dynamic environments. The particle filter is a useful tool for nonlinear object tracking with non-Gaussian noise. Nonlinear object tracking needs the real-time processing capability of the particle filter. While the number in a traditional particle filter is fixed, that can lead to a lot of unnecessary computation. To address this issue, a confidence-level-based new adaptive particle filter (NAPF) algorithm is proposed in this paper. In this algorithm the idea of confidence interval is utilized. The least number of particles for the next time instant is estimated according to the confidence level and the variance of the estimated state. Accordingly, an improved systematic re-sampling algorithm is utilized for the new improved particle filter. NAPF can effectively reduce the computation while ensuring the accuracy of nonlinear object tracking. The simulation results and the ball tracking results of the robot verify the effectiveness of the algorithm

Minimum s-Excess Graph for Segmenting and Tracking Multiple Borders with HMM

We present a novel HMM based approach to simultaneous segmentation of vessel walls in Lymphatic confocal images. The vessel borders are parameterized using RBFs to minimize the number of tracking points. The proposed method tracks the hidden states that indicate border locations for both the inner and outer walls. The observation for both borders is obtained using edge-based features from steerable filters. Two separate Gaussian probability distributions for the vessel borders and background are used to infer the emission probability, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into a minimization of an s-excess graph cost, with each node in the graph corresponding to a hidden state and the weight for each node being defined by its emission probability. We define the inter-relations between neighboring nodes based on the transmission probability. We present both qualitative and quantitative analysis in comparison to the popular Viterbi algorithm

A Novel and Effective Short Track Speed Skating Tracking System

This dissertation proposes a novel and effective system for tracking high-speed skaters. A novel registration method is employed to automatically discover key frames to build the panorama. Then, the homography between a frame and the real world rink can be generated accordingly. Aimed at several challenging tracking problems of short track skating, a novel multiple-objects tracking approach is proposed which includes: Gaussian mixture models (GMMs), evolving templates, constrained dynamical model, fuzzy model, multiple templates initialization, and evolution. The outputs of the system include spatialtemporal trajectories, velocity analysis, and 2D reconstruction animations. The tracking accuracy is about 10 cm (2 pixels). Such information is invaluable for sports experts. Experimental results demonstrate the effectiveness and robustness of the proposed system

PERFORMANCE METRICS IN VIDEO SURVEILLANCE SYSTEM

Video surveillance is an active research topic in computer vision. One of the areas that are being actively researched is on the abilities of surveillance systems to track multiple objects over time in occluded scenes and to keep a consistent identity for each target object. These abilities enable a surveillance system to provide crucial information about moving objects behaviour and interaction. This survey reviews the recent developments in moving object detection and also different techniques and approaches in multiple objects tracking that have been developed by researchers. The algorithms and filters that can be incorporated in tracking multiples object to solve the occluded and natural busy scenes in surveillance systems are also reviewed in this paper. This survey is meant to provide researchers in the field with a summary of progress achieved up to date in multiple moving objects tracking. Despite recent progress in computer vision and other related areas, there are still major technical challenges that need to be solved before reliable automated video surveillance system can be realized

Real-Time Implementation of Time-Varying Surface Prediction and Projection

Spatial augmented reality makes use of projectors to transform an object into a display surface. However, for time-varying, non-rigid surfaces this can prove to be difficult, and often leads to image distortion. In order to avoid this highly accurate measurements of the surface are required. Traditional methods of measuring surface deformations are inadequate due to noise as well as potential sources of time delay, such as projector lag. To get more accurate results, a mass spring model can be used to simulate the dynamics of the time-varying surface. This model can be put into a nonlinear state space form to get a first order differential equation. Numerical integration techniques can then be used to solve the differential equation presented. In order to reduce uncertainty in the model generated a filtering algorithm can be used. Both, the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) are evaluated as potential candidates. To be able to run these filters in real time a reduced order model is developed. This enables the use of fewer mass nodes in the model, allowing for faster compute times. Additionally, to reduce visual error, an optimal node placement algorithm is used. This ensures that the surface generated by the mass spring mesh closely matches the real, curved surface of the system, minimizing error. The EKF and CKF algorithms are implemented onto a hanging cloth system perturbed by an oscillating fan. A parameter identification technique is used to create a model that accurately represents this hanging cloth system. Additionally, noise parameters of the EKF and CKF are adjusted to compensate for modeling errors and sensor noise. Finally, The mean squared error of the EKF and CKF algorithms are compared to evaluate their effectiveness. Both algorithms provide satisfactory results for use in spatial augmented reality applications. However, in all cases tested the CKF is shown to have significantly lower error values. Although the CKF algorithm is shown to be more accurate than its EKF counterpart, its computation time is much larger. However, the computation time required is still within the threshold of being able to perform real-time estimation at up to 100Hz. Furthermore, due to the nature of the construction of the CKF, it can be applied as a multi-threaded workload to significantly reduce computation time. Therefore, the implementation of a CKF algorithm can be used to accurately estimate the positions of a measured surface for use in spatial augmented reality

Prediction for Projection on Time-Varying Surfaces

In spatial augmented reality applications, when video projectors display images on time-varying, non-planar surfaces, rather than on flat, rigid surfaces, undesired image distortion may occur. For applications where realism is of the utmost importance, such as surgical simulations, image distortion can significantly detract from the user experience. To combat this, the time-varying surface can be modelled using a mass-spring model, commonly used for simulating deformable objects in computer graphics. The mass-spring model can be formulated into a nonlinear state space equation that describes the dynamics of discrete points making up the surface of the object. Two simulation techniques are used to verify the model and to determine the best approach for real-time simulations. To project images in real-time onto quickly changing surfaces, an extended Kalman filter (EKF) prediction algorithm is developed to predict the position of the deforming surface, at a specified point in time, T_s seconds, in the future. Using the linearized mass-spring system, the EKF is formulated and tested upon two simulation scenarios. The simulation scenarios include a falling cloth with added process noise, and a cloth perturbed by random viscous forces. Using mean squared error, the results show the EKF predictions and simulation outputs converge within a narrow band. For each scenario, the parameters of the EKF are manually tuned to improve the accuracy of the predictions. Experimental data is collected by measuring the movement of cloth-like materials to verify the effectiveness of the prediction algorithm. Specifically, cloth movement data is captured using infra-red markers and motion capture software. The EKF prediction algorithm is run on the experimental data producing near convergent results between the predictions and the measurements. When the physical surface is changing noticeably and quickly, compared to the projector's drawing rate, additional distortion may occur. An inter-frame prediction algorithm is developed to further predict the position of discrete points at their corresponding projection times. This is most useful when the prediction algorithm produces predictions slower than the drawing rate of the projector (T_s>1/fps). When implementing the EKF in real-time, there is a trade-off between speed and accuracy. If the number of discrete points is large, the EKF is required to solve a large system of equations. To combat this, nonlinear optimization techniques are used to find parameters that reduce the number of states while maintaining system dynamics. This results in a sparser, more computationally efficient model with similar physical behaviour to the original system. Applications for time-varying surface prediction include surgical simulations, projection for entertainment and advertising, and other spatial augmented reality applications

Combinatorial optimisation for arterial image segmentation.

Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods

2D and 3D segmentation of medical images.

"Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve.