Simplified multitarget tracking using the PHD filter for microscopic video data

The probability hypothesis density (PHD) filter from the theory of random finite sets is a well-known method for multitarget tracking. We present the Gaussian mixture (GM) and improved sequential Monte Carlo implementations of the PHD filter for visual tracking. These implementations are shown to provide advantages over previous PHD filter implementations on visual data by removing complications such as clustering and data association and also having beneficial computational characteristics. The GM-PHD filter is deployed on microscopic visual data to extract trajectories of free-swimming bacteria in order to analyze their motion. Using this method, a significantly larger number of tracks are obtained than was previously possible. This permits calculation of reliable distributions for parameters of bacterial motion. The PHD filter output was tested by checking agreement with a careful manual analysis. A comparison between the PHD filter and alternative tracking methods was carried out using simulated data, demonstrating superior performance by the PHD filter in a range of realistic scenarios

Space-based relative multitarget tracking

Access to space has expanded dramatically over the past decade. The growing popularity of small satellites, specifically cubesats, and the following launch initiatives have resulted in exponentially growing launch numbers into low Earth orbit. This growing congestion in space has punctuated the need for local space monitoring and autonomous satellite inspection. This work describes the development of a framework for monitoring local space and tracking multiple objects concurrently in a satellite\u27s neighborhood. The development of this multitarget tracking systems has produced collateral developments in numerical methods, relative orbital mechanics, and initial relative orbit determination. This work belongs to a class of navigation known as angles-only navigation, in which angles representing the direction to the target are measured but no range measurements are available. A key difference between this work and traditional angles-only relative navigation research is that angle measurements are collected from two separate cameras simultaneously. Such measurements, when coupled with the known location and orientation of the stereo cameras, can be used to resolve the relative range component of a target\u27s position. This fact is exploited to form initial statistical representations of the targets\u27 relative states, which are subsequently refined in Bayesian single-target and multitarget frameworks --Abstract, page iii

Intelligent video object tracking in large public environments

This Dissertation addresses the problem of video object tracking in large public environments, and was developed within the context of a partnership between ISCTE-IUL and THALES1 object. This partnership aimed at developing a new approach to video tracking, based on a simple tracking algorithm aided by object position estimations to deal with the harder cases of video object tracking. This proposed approach has been applied successfully in the TRAPLE2 project developed at THALES where the main focus is the real-time monitoring of public spaces and the tracking of moving objects (i.e., persons). The proposed low-processing tracking solution woks as follows: after the detection step, the various objects in the visual scene are tracked through their centres of mass (centroids) that, typically, exhibit little variations along close apart video frames. After this step, some heuristics are applied to the results to maintain coherent the identification of the video objects and estimate their positions in cases of uncertainties, e.g., occlusions, which is one of the major novelties proposed in this Dissertation. The proposed approach was tested with relevant test video sequences representing real video monitoring scenes and the obtained results showed that this approach is able to track multiple persons in real-time with reasonable computational power.Esta dissertação aborda o problema do seguimento de objectos vídeo em ambientes públicos de grande dimensão e foi desenvolvida no contexto de uma parceria entre o ISCTE-IUL e a THALES. Esta parceria visou o desenvolvimento de uma nova abordagem ao seguimento de objectos de vídeo baseada num processamento de vídeo simples em conjunto com a estimação da posição dos objectos nos casos mais difíceis de efectuar o seguimento. Esta abordagem foi aplicada com sucesso no âmbito do projecto TRAPLE desenvolvido pela THALES onde um dos principais enfoques é o seguimento de múltiplos objectos de vídeo em tempo real em espaços públicos, tendo como objectivo o seguimento de pessoas que se movam ao longo desse espaço. A solução de baixo nível de processamento proposta funciona do seguinte modo: após o passo de detecção de objectos, os diversos objectos detectados na cena são seguidos através dos seus centros de massa que, normalmente, apresentam poucas variações ao longo de imagens consecutivas de vídeo. Após este passo, algumas heurísticas são aplicadas aos resultados mantendo a identificação dos objectos de vídeo coerente e estimando as suas posições em casos de incertezas (e.g., oclusões) que é uma das principais novidades propostas nesta dissertação. A abordagem proposta foi testada com várias sequências de vídeo de teste representando cenas reais de videovigilância e os resultados obtidos mostraram que esta abordagem é capaz de seguir várias pessoas em tempo real com um nível de processamento moderado

Data-driven probability hypothesis density filter for visual tracking

10.1109/TCSVT.2008.927105IEEE Transactions on Circuits and Systems for Video Technology1881085-1095ITCT

State Estimation and Smoothing for the Probability Hypothesis Density Filter

Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements. However, in many important cases the number of targets is also unknown, and has also to be estimated from data. The Probability Hypothesis Density (PHD) filter is an effective approach for this problem. The method uses a first-order moment approximation to develop a recursive algorithm for the optimal Bayesian filter. The PHD recursion can implemented in closed form in some restricted cases, and more generally using Sequential Monte Carlo (SMC) methods. The assumptions made in the PHD filter are appealing for computational reasons in real-time tracking implementations. These are only justifiable when the signal to noise ratio (SNR) of a single target is high enough that remediates the loss of information from the approximation. Although the original derivation of the PHD filter is based on functional expansions of belief-mass functions, it can also be developed by exploiting elementary constructions of Poisson processes. This thesis presents novel strategies for improving the Sequential Monte Carlo implementation of PHD filter using the point process approach. Firstly, we propose a post-processing state estimation step for the PHD filter, using Markov Chain Monte Carlo methods for mixture models. Secondly, we develop recursive Bayesian smoothing algorithms using the approximations of the filter backwards in time. The purpose of both strategies is to overcome the problems arising from the PHD filter assumptions. As a motivating example, we analyze the performance of the methods for the difficult problem of person tracking in crowded environment

Novel data association methods for online multiple human tracking

PhD ThesisVideo-based multiple human tracking has played a crucial role in many applications such as intelligent video surveillance, human behavior analysis, and health-care systems. The detection based tracking framework has become the dominant paradigm in this research eld, and the major task is to accurately perform the data association between detections across the frames. However, online multiple human tracking, which merely relies on the detections given up to the present time for the data association, becomes more challenging with noisy detections, missed detections, and occlusions. To address these challenging problems, there are three novel data association methods for online multiple human tracking are presented in this thesis, which are online group-structured dictionary learning, enhanced detection reliability and multi-level cooperative fusion. The rst proposed method aims to address the noisy detections and occlusions. In this method, sequential Monte Carlo probability hypothesis density (SMC-PHD) ltering is the core element for accomplishing the tracking task, where the measurements are produced by the detection based tracking framework. To enhance the measurement model, a novel adaptive gating strategy is developed to aid the classi cation of measurements. In addition, online group-structured dictionary learning with a maximum voting method is proposed to estimate robustly the target birth intensity. It enables the new-born targets in the tracking process to be accurately initialized from noisy sensor measurements. To improve the adaptability of the group-structured dictionary to target appearance changes, the simultaneous codeword optimization (SimCO) algorithm is employed for the dictionary update. The second proposed method relates to accurate measurement selection of detections, which is further to re ne the noisy detections prior to the tracking pipeline. In order to achieve more reliable measurements in the Gaussian mixture (GM)-PHD ltering process, a global-to-local enhanced con dence rescoring strategy is proposed by exploiting the classi cation power of a mask region-convolutional neural network (R-CNN). Then, an improved pruning algorithm namely soft-aggregated non-maximal suppression (Soft-ANMS) is devised to further enhance the selection step. In addition, to avoid the misuse of ambiguous measurements in the tracking process, person re-identi cation (ReID) features driven by convolutional neural networks (CNNs) are integrated to model the target appearances. The third proposed method focuses on addressing the issues of missed detections and occlusions. This method integrates two human detectors with di erent characteristics (full-body and body-parts) in the GM-PHD lter, and investigates their complementary bene ts for tracking multiple targets. For each detector domain, a novel discriminative correlation matching (DCM) model for integration in the feature-level fusion is proposed, and together with spatio-temporal information is used to reduce the ambiguous identity associations in the GM-PHD lter. Moreover, a robust fusion center is proposed within the decision-level fusion to mitigate the sensitivity of missed detections in the fusion process, thereby improving the fusion performance and tracking consistency. The e ectiveness of these proposed methods are investigated using the MOTChallenge benchmark, which is a framework for the standardized evaluation of multiple object tracking methods. Detailed evaluations on challenging video datasets, as well as comparisons with recent state-of-the-art techniques, con rm the improved multiple human tracking performance

Multi-object tracking in video using labeled random finite sets

The safety of industrial mobile platforms (such as fork lifts and boom lifts) is of major concern in the world today as industry embraces the concepts of Industry 4.0. The existing safety methods are predominantly based on Radio Frequency Identification (RFID) technology and therefore can only determine the distance at which a pedestrian who is wearing an RFID tag is standing. Other methods use expensive laser scanners to map the surrounding and warn the driver accordingly. The aim of this research project is to improve the safety of industrial mobile platforms, by detecting and tracking pedestrians in the path of the mobile platform, using readily available cheap camera modules. In order to achieve this aim, this research focuses on multi-object tracking which is one of the most ubiquitously addressed problems in the field of \textit{Computer Vision}. Algorithms that can track targets under severe conditions, such as varying number of objects, occlusion, illumination changes and abrupt movements of the objects are investigated in this research project. Furthermore, a substantial focus is given to improving the accuracy and, performance and to handling misdetections and false alarms. In order to formulate these algorithms, the recently introduced concept of Random Finite Sets (RFS) is used as the underlying mathematical framework. The algorithms formulated to meet the above criteria were tested on standard visual tracking datasets as well as on a dataset which was created by our research group, for performance and accuracy using standard performance and accuracy metrics that are widely used in the computer vision literature. These results were compared with numerous state-of-the-art methods and are shown to outperform or perform favourably in terms of the metrics mentioned above

Methods for Automated Neuron Image Analysis

Knowledge of neuronal cell morphology is essential for performing specialized analyses in the endeavor to understand neuron behavior and unravel the underlying principles of brain function. Neurons can be captured with a high level of detail using modern microscopes, but many neuroscientific studies require a more explicit and accessible representation than offered by the resulting images, underscoring the need for digital reconstruction of neuronal morphology from the images into a tree-like graph structure. This thesis proposes new computational methods for automated detection and reconstruction of neurons from fluorescence microscopy images. Specifically, the successive chapters describe and evaluate original solutions to problems such as the detection of landmarks (critical points) of the neuronal tree, complete tracing and reconstruction of the tree, and the detection of regions containing neurons in high-content screens