Decentralized kalman filter approach for multi-sensor multi-target tracking problems

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Doğru pozisyon ve hedeflerin sayısı hava trafik kontrol ve füze savunması için çok önemli bilgilerdir. Bu çalışma, çoklu sensorlü çoklu hedef takibi sistemlerindeki veri füzyonu ve durum tahmini problemlerı için dağıtık Kalman Filtreleme Algoritması sunmaktadır. Problem, radar olarak her biri kendi veri işleme birimine sahip aktif sensörlerin hedef alanını gözlemlemesini esas almaktadır. Bu durumda her bir sistemin iz sayısı olacaktır. Çalışmada önerilen dağıtık Kalman Filtresi, başta füze sistemleri olmak üzere savunma sistemlerinde hareketli hedeflerin farklı sensörlerle izlerini kestirmek ve farklı hedefleri ayrıd etmek için kullanmaktır. Önerilen teknik, çoklu sensör sisteminden gelen verileri işleyen iki aşamalı veri işleme yaklaşımını içermektedir. İlk aşamada, her yerel işlemci kendi verilerini ve standart Kalman filtresi ise en iyi kestirimi yapmak için kullanılmaktadır. Sonraki aşamada bu kestirimler en iyi küresel bir kestirimi yapmak amacıyla dağıtık işlem modunda elde edilir. Bu çalışmada iki radar sistemi iki yerel Kalman filtresi ile uçakların pozisyonunu kestirmek amacıyla kullanılmakta, ardından bu kestirimler merkez işlemciye iletilmektedir. Merkez işlemci doğrulama maksadıyla bu bilgileri birleştirip küresel bir kestirim üretmektedir. Önerilen model uygulama olarak dört senaryo üzerinde test edildi. İlk senaryoda, tek bir hedef iki sensor tarafından izlenirken, ikincisinde, iki hedeften oluşan uzay herhangi bir sensor tarafından izlenmekte, üçüncüsünde, iki hedefin de herhangi bir sensor tarafından aynı anda izlenmesi, son olarak ise iki sensörden her birinin toplam üç hedeften herhangi ikisini izlediği senaryo göz önüne alınmıştır. Önerilen tekniğin performansı hata kovaryans matrisi kullanılarak değerlendirildi ve yüksek doğruluk ve optimal kestirim elde edildi. Uygulama sonuçları önerilen tekniğin yeteneğinin, yerel sensörlerce belirlenen ortak hedeflerin merkezi sistem tarafından ayırd edilebildiğini göstermiştir.For air traffic control and missile defense, the accurate position and the numbers of targets are the most important information needed. This thesis presents a decentralized kalman filtering algorithm (DKF) for data fusion and state estimation problems in multi-sensor multi-target tracking system. The problem arises when several sensors carry out surveillance over a certain area and each sensor has its own data processing system. In this situation, each system has a number of tracks. The DKF is used to estimate and separate the tracks from different sensors represent the targets, when the ability to track targets is essential in missile defense. The proposed technique is a two stage data processing technique which processes data from multi sensor system. In the first stage, each local processor uses its own data to make the best local estimation using standard kalman filter and then these estimations are then obtained in parallel processing mode to make best global estimation. In this work, two radar systems are used as sensors with two local Kalman filters to estimate the position of an aircraft and then they transmit these estimations to a central processor, which combines this information to produce a global estimation. The proposed model is tested on four scenarios, firstly, when there is one target and the two sensors are tracking the same target, secondly, when there are two targets and any sensor is tracking one of them, thirdly, when there are two targets and any sensor is tracking both of them and finally, when two sensors are used to track three targets and any sensor tracks any two of them. The performance of the proposed technique is evaluated using measures such as the error covariance matrix and it gave high accuracy and optimal estimation. The experimental results showed that the proposed method has the ability to separate the joint targets detected by the local sensors

Radar networks: A review of features and challenges

Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio

Efficient Min-cost Flow Tracking with Bounded Memory and Computation

This thesis is a contribution to solving multi-target tracking in an optimal fashion for real-time demanding computer vision applications. We introduce a challenging benchmark, recorded with our autonomous driving platform AnnieWAY. Three main challenges of tracking are addressed: Solving the data association (min-cost flow) problem faster than standard solvers, extending this approach to an online setting, and making it real-time capable by a tight approximation of the optimal solution

Fuzzy region assignment for visual tracking

In this work we propose a new approach based on fuzzy concepts and heuristic reasoning to deal with the visual data association problem in real time, considering the particular conditions of the visual data segmented from images, and the integration of higher-level information in the tracking process such as trajectory smoothness, consistency of information, and protection against predictable interactions such as overlap/occlusion, etc. The objects' features are estimated from the segmented images using a Bayesian formulation, and the regions assigned to update the tracks are computed through a fuzzy system to integrate all the information. The algorithm is scalable, requiring linear computing resources with respect to the complexity of scenarios, and shows competitive performance with respect to other classical methods in which the number of evaluated alternatives grows exponentially with the number of objects.Research supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB and CAM MADRINET S-0505/TIC/0255.publicad

Single to multiple target, multiple type visual tracking

Visual tracking is a key task in applications such as intelligent surveillance, humancomputer interaction (HCI), human-robot interaction (HRI), augmented reality (AR), driver assistance systems, and medical applications. In this thesis, we make three main novel contributions for target tracking in video sequences. First, we develop a long-term model-free single target tracking by learning discriminative correlation filters and an online classifier that can track a target of interest in both sparse and crowded scenes. In this case, we learn two different correlation filters, translation and scale correlation filters, using different visual features. We also include a re-detection module that can re-initialize the tracker in case of tracking failures due to long-term occlusions. Second, a multiple target, multiple type filtering algorithm is developed using Random Finite Set (RFS) theory. In particular, we extend the standard Probability Hypothesis Density (PHD) filter for multiple type of targets, each with distinct detection properties, to develop multiple target, multiple type filtering, N-type PHD filter, where N ≥ 2, for handling confusions that can occur among target types at the measurements level. This method takes into account not only background false positives (clutter), but also confusions between target detections, which are in general different in character from background clutter. Then, under the assumptions of Gaussianity and linearity, we extend Gaussian mixture (GM) implementation of the standard PHD filter for the proposed N-type PHD filter termed as N-type GM-PHD filter. Third, we apply this N-type GM-PHD filter to real video sequences by integrating object detectors’ information into this filter for two scenarios. In the first scenario, a tri-GM-PHD filter is applied to real video sequences containing three types of multiple targets in the same scene, two football teams and a referee, using separate but confused detections. In the second scenario, we use a dual GM-PHD filter for tracking pedestrians and vehicles in the same scene handling their detectors’ confusions. For both cases, Munkres’s variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames. We make extensive evaluations of these developed algorithms and find out that our methods outperform their corresponding state-of-the-art approaches by a large margin.EPSR

Adaptive visual sampling

PhDVarious visual tasks may be analysed in the context of sampling from the visual field. In visual psychophysics, human visual sampling strategies have often been shown at a high-level to be driven by various information and resource related factors such as the limited capacity of the human cognitive system, the quality of information gathered, its relevance in context and the associated efficiency of recovering it. At a lower-level, we interpret many computer vision tasks to be rooted in similar notions of contextually-relevant, dynamic sampling strategies which are geared towards the filtering of pixel samples to perform reliable object association. In the context of object tracking, the reliability of such endeavours is fundamentally rooted in the continuing relevance of object models used for such filtering, a requirement complicated by realworld conditions such as dynamic lighting that inconveniently and frequently cause their rapid obsolescence. In the context of recognition, performance can be hindered by the lack of learned context-dependent strategies that satisfactorily filter out samples that are irrelevant or blunt the potency of models used for discrimination. In this thesis we interpret the problems of visual tracking and recognition in terms of dynamic spatial and featural sampling strategies and, in this vein, present three frameworks that build on previous methods to provide a more flexible and effective approach. Firstly, we propose an adaptive spatial sampling strategy framework to maintain statistical object models for real-time robust tracking under changing lighting conditions. We employ colour features in experiments to demonstrate its effectiveness. The framework consists of five parts: (a) Gaussian mixture models for semi-parametric modelling of the colour distributions of multicolour objects; (b) a constructive algorithm that uses cross-validation for automatically determining the number of components for a Gaussian mixture given a sample set of object colours; (c) a sampling strategy for performing fast tracking using colour models; (d) a Bayesian formulation enabling models of object and the environment to be employed together in filtering samples by discrimination; and (e) a selectively-adaptive mechanism to enable colour models to cope with changing conditions and permit more robust tracking. Secondly, we extend the concept to an adaptive spatial and featural sampling strategy to deal with very difficult conditions such as small target objects in cluttered environments undergoing severe lighting fluctuations and extreme occlusions. This builds on previous work on dynamic feature selection during tracking by reducing redundancy in features selected at each stage as well as more naturally balancing short-term and long-term evidence, the latter to facilitate model rigidity under sharp, temporary changes such as occlusion whilst permitting model flexibility under slower, long-term changes such as varying lighting conditions. This framework consists of two parts: (a) Attribute-based Feature Ranking (AFR) which combines two attribute measures; discriminability and independence to other features; and (b) Multiple Selectively-adaptive Feature Models (MSFM) which involves maintaining a dynamic feature reference of target object appearance. We call this framework Adaptive Multi-feature Association (AMA). Finally, we present an adaptive spatial and featural sampling strategy that extends established Local Binary Pattern (LBP) methods and overcomes many severe limitations of the traditional approach such as limited spatial support, restricted sample sets and ad hoc joint and disjoint statistical distributions that may fail to capture important structure. Our framework enables more compact, descriptive LBP type models to be constructed which may be employed in conjunction with many existing LBP techniques to improve their performance without modification. The framework consists of two parts: (a) a new LBP-type model known as Multiscale Selected Local Binary Features (MSLBF); and (b) a novel binary feature selection algorithm called Binary Histogram Intersection Minimisation (BHIM) which is shown to be more powerful than established methods used for binary feature selection such as Conditional Mutual Information Maximisation (CMIM) and AdaBoost

Particle Filters for Colour-Based Face Tracking Under Varying Illumination

Automatic human face tracking is the basis of robotic and active vision systems used for facial feature analysis, automatic surveillance, video conferencing, intelligent transportation, human-computer interaction and many other applications. Superior human face tracking will allow future safety surveillance systems which monitor drowsy drivers, or patients and elderly people at the risk of seizure or sudden falls and will perform with lower risk of failure in unexpected situations. This area has actively been researched in the current literature in an attempt to make automatic face trackers more stable in challenging real-world environments. To detect faces in video sequences, features like colour, texture, intensity, shape or motion is used. Among these feature colour has been the most popular, because of its insensitivity to orientation and size changes and fast process-ability. The challenge of colour-based face trackers, however, has been dealing with the instability of trackers in case of colour changes due to the drastic variation in environmental illumination. Probabilistic tracking and the employment of particle filters as powerful Bayesian stochastic estimators, on the other hand, is increasing in the visual tracking field thanks to their ability to handle multi-modal distributions in cluttered scenes. Traditional particle filters utilize transition prior as importance sampling function, but this can result in poor posterior sampling. The objective of this research is to investigate and propose stable face tracker capable of dealing with challenges like rapid and random motion of head, scale changes when people are moving closer or further from the camera, motion of multiple people with close skin tones in the vicinity of the model person, presence of clutter and occlusion of face. The main focus has been on investigating an efficient method to address the sensitivity of the colour-based trackers in case of gradual or drastic illumination variations. The particle filter is used to overcome the instability of face trackers due to nonlinear and random head motions. To increase the traditional particle filter\u27s sampling efficiency an improved version of the particle filter is introduced that considers the latest measurements. This improved particle filter employs a new colour-based bottom-up approach that leads particles to generate an effective proposal distribution. The colour-based bottom-up approach is a classification technique for fast skin colour segmentation. This method is independent to distribution shape and does not require excessive memory storage or exhaustive prior training. Finally, to address the adaptability of the colour-based face tracker to illumination changes, an original likelihood model is proposed based of spatial rank information that considers both the illumination invariant colour ordering of a face\u27s pixels in an image or video frame and the spatial interaction between them. The original contribution of this work lies in the unique mixture of existing and proposed components to improve colour-base recognition and tracking of faces in complex scenes, especially where drastic illumination changes occur. Experimental results of the final version of the proposed face tracker, which combines the methods developed, are provided in the last chapter of this manuscript