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

A Comprehensive Mapping and Real-World Evaluation of Multi-Object Tracking on Automated Vehicles

Multi-Object Tracking (MOT) is a field critical to Automated Vehicle (AV) perception systems. However, it is large, complex, spans research fields, and lacks resources for integration with real sensors and implementation on AVs. Factors such those make it difficult for new researchers and practitioners to enter the field. This thesis presents two main contributions: 1) a comprehensive mapping for the field of Multi-Object Trackers (MOTs) with a specific focus towards Automated Vehicles (AVs) and 2) a real-world evaluation of an MOT developed and tuned using COTS (Commercial Off-The-Shelf) software toolsets. The first contribution aims to give a comprehensive overview of MOTs and various MOT subfields for AVs that have not been presented as wholistically in other papers. The second contribution aims to illustrate some of the benefits of using a COTS MOT toolset and some of the difficulties associated with using real-world data. This MOT performed accurate state estimation of a target vehicle through the tracking and fusion of data from a radar and vision sensor using a Central-Level Track Processing approach and a Global Nearest Neighbors assignment algorithm. It had an 0.44 m positional Root Mean Squared Error (RMSE) over a 40 m approach test. It is the authors\u27 hope that this work provides an overview of the MOT field that will help new researchers and practitioners enter the field. Additionally, the author hopes that the evaluation section illustrates some difficulties of using real-world data and provides a good pathway for developing and deploying MOTs from software toolsets to Automated Vehicles

Localisation and tracking of people using distributed UWB sensors

In vielen Überwachungs- und Rettungsszenarien ist die Lokalisierung und Verfolgung von Personen in Innenräumen auf nichtkooperative Weise erforderlich. Für die Erkennung von Objekten durch Wände in kurzer bis mittlerer Entfernung, ist die Ultrabreitband (UWB) Radartechnologie aufgrund ihrer hohen zeitlichen Auflösung und Durchdringungsfähigkeit Erfolg versprechend. In dieser Arbeit wird ein Prozess vorgestellt, mit dem Personen in Innenräumen mittels UWB-Sensoren lokalisiert werden können. Er umfasst neben der Erfassung von Messdaten, Abstandschätzungen und dem Erkennen von Mehrfachzielen auch deren Ortung und Verfolgung. Aufgrund der schwachen Reflektion von Personen im Vergleich zum Rest der Umgebung, wird zur Personenerkennung zuerst eine Hintergrundsubtraktionsmethode verwendet. Danach wird eine konstante Falschalarmrate Methode zur Detektion und Abstandschätzung von Personen angewendet. Für Mehrfachziellokalisierung mit einem UWB-Sensor wird eine Assoziationsmethode entwickelt, um die Schätzungen des Zielabstandes den richtigen Zielen zuzuordnen. In Szenarien mit mehreren Zielen kann es vorkommen, dass ein näher zum Sensor positioniertes Ziel ein anderes abschattet. Ein Konzept für ein verteiltes UWB-Sensornetzwerk wird vorgestellt, in dem sich das Sichtfeld des Systems durch die Verwendung mehrerer Sensoren mit unterschiedlichen Blickfeldern erweitert lässt. Hierbei wurde ein Prototyp entwickelt, der durch Fusion von Sensordaten die Verfolgung von Mehrfachzielen in Echtzeit ermöglicht. Dabei spielen insbesondere auch Synchronisierungs- und Kooperationsaspekte eine entscheidende Rolle. Sensordaten können durch Zeitversatz und systematische Fehler gestört sein. Falschmessungen und Rauschen in den Messungen beeinflussen die Genauigkeit der Schätzergebnisse. Weitere Erkenntnisse über die Zielzustände können durch die Nutzung zeitlicher Informationen gewonnen werden. Ein Mehrfachzielverfolgungssystem wird auf der Grundlage des Wahrscheinlichkeitshypothesenfilters (Probability Hypothesis Density Filter) entwickelt, und die Unterschiede in der Systemleistung werden bezüglich der von den Sensoren ausgegebene Informationen, d.h. die Fusion von Ortungsinformationen und die Fusion von Abstandsinformationen, untersucht. Die Information, dass ein Ziel detektiert werden sollte, wenn es aufgrund von Abschattungen durch andere Ziele im Szenario nicht erkannt wurde, wird als dynamische Überdeckungswahrscheinlichkeit beschrieben. Die dynamische Überdeckungswahrscheinlichkeit wird in das Verfolgungssystem integriert, wodurch weniger Sensoren verwendet werden können, während gleichzeitig die Performanz des Schätzers in diesem Szenario verbessert wird. Bei der Methodenauswahl und -entwicklung wurde die Anforderung einer Echtzeitanwendung bei unbekannten Szenarien berücksichtigt. Jeder untersuchte Aspekt der Mehrpersonenlokalisierung wurde im Rahmen dieser Arbeit mit Hilfe von Simulationen und Messungen in einer realistischen Umgebung mit UWB Sensoren verifiziert.Indoor localisation and tracking of people in non-cooperative manner is important in many surveillance and rescue applications. Ultra wideband (UWB) radar technology is promising for through-wall detection of objects in short to medium distances due to its high temporal resolution and penetration capability. This thesis tackles the problem of localisation of people in indoor scenarios using UWB sensors. It follows the process from measurement acquisition, multiple target detection and range estimation to multiple target localisation and tracking. Due to the weak reflection of people compared to the rest of the environment, a background subtraction method is initially used for the detection of people. Subsequently, a constant false alarm rate method is applied for detection and range estimation of multiple persons. For multiple target localisation using a single UWB sensor, an association method is developed to assign target range estimates to the correct targets. In the presence of multiple targets it can happen that targets closer to the sensor induce shadowing over the environment hindering the detection of other targets. A concept for a distributed UWB sensor network is presented aiming at extending the field of view of the system by using several sensors with different fields of view. A real-time operational prototype has been developed taking into consideration sensor cooperation and synchronisation aspects, as well as fusion of the information provided by all sensors. Sensor data may be erroneous due to sensor bias and time offset. Incorrect measurements and measurement noise influence the accuracy of the estimation results. Additional insight of the targets states can be gained by exploiting temporal information. A multiple person tracking framework is developed based on the probability hypothesis density filter, and the differences in system performance are highlighted with respect to the information provided by the sensors i.e. location information fusion vs range information fusion. The information that a target should have been detected when it is not due to shadowing induced by other targets is described as dynamic occlusion probability. The dynamic occlusion probability is incorporated into the tracking framework, allowing fewer sensors to be used while improving the tracker performance in the scenario. The method selection and development has taken into consideration real-time application requirements for unknown scenarios at every step. Each investigated aspect of multiple person localization within the scope of this thesis has been verified using simulations and measurements in a realistic environment using M-sequence UWB sensors

Joint Registration and Fusion of an Infra-Red Camera and Scanning Radar in a Maritime Context

The number of nodes in sensor networks is continually increasing, and maintaining accurate track estimates inside their common surveillance region is a critical necessity. Modern sensor platforms are likely to carry a range of different sensor modalities, all providing data at differing rates, and with varying degrees of uncertainty. These factors complicate the fusion problem as multiple observation models are required, along with a dynamic prediction model. However, the problem is exacerbated when sensors are not registered correctly with respect to each other, i.e. if they are subject to a static or dynamic bias. In this case, measurements from different sensors may correspond to the same target, but do not correlate with each other when in the same Frame of Reference (FoR), which decreases track accuracy. This paper presents a method to jointly estimate the state of multiple targets in a surveillance region, and to correctly register a radar and an Infrared Search and Track (IRST) system onto the same FoR to perform sensor fusion. Previous work using this type of parent-offspring process has been successful when calibrating a pair of cameras, but has never been attempted on a heterogeneous sensor network, nor in a maritime environment. This article presents results on both simulated scenarios and a segment of real data that show a significant increase in track quality in comparison to using incorrectly calibrated sensors or single-radar only

Contextual information aided target tracking and path planning for autonomous ground vehicles

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion