New multiple target tracking strategy using domain knowledge and optimisation

This paper proposes an environment-dependent vehicle dynamic modeling approach considering interactions between the noisy control input of a dynamic model and the environment in order to make best use of domain knowledge. Based on this modeling, a new domain knowledge-aided moving horizon estimation (DMHE) method is proposed for ground moving target tracking. The proposed method incorporates different types of domain knowledge in the estimation process considering both environmental physical constraints and interaction behaviors between targets and the environment. Furthermore, in order to deal with a data association ambiguity problem of multiple-target tracking in a cluttered environment, the DMHE is combined with a multiple-hypothesis tracking structure. Numerical simulation results show that the proposed DMHE-based method and its extension could achieve better performance than traditional tracking methods which utilize no domain knowledge or simple physical constraint information only

Multitarget Tracking Menggunakan Multiple Hypothesis Tracking Dengan Clustering TIME Window Data Radar

Sistem radar dibagi menjadi dua jenis yaitu sistem radar sipil dan sistem radar militer. Kedua jenis sistem radar tersebut memiliki kesamaan yaitu telah digunakan untuk pemantauan lalu lintas udara. Pesawat yang dipantau di udara saat ini mengalami jumlah peningkatan yang besar sehingga untuk memudahkan pemantauannya diperlukan suatu sistem yang dinamakan multitarget aircraft tracking. Penelitian ini bertujuan untuk mendapatkan algoritma multitarget tracking (MTT) yang valid, yaitu dengan menggunakan kombinasi preprocessing data radar dengan clustering time window (CTW) dan algoritma Multiple Hypothesis Tracking (MHT).Penelitian ini diawali dengan penyiapan data rekaman radar yang direkam langsung. Kemudian data rekaman tersebut disimulasikan dengan algoritma yang telah dirancang. Pengecekan kemampuan algoritma tersebut dilakukan dengan membandingkannya dengan MHT tanpa clustering time window. Uji coba dilakukan dengan memakai data rekaman berdurasi kurang lebih 18 menit. Uji coba tersebut menghasilkan nilai correct target sebesar 87.66%, undetected target sebesar 12.81%, maintain target sebesar 80.5% dan inexisting target sebesar 23.65%. Dari hasil uji coba menunjukkan bahwa metode yang diusulkan lebih bagus jika dibandingkan dengan metode MHT tanpa CTW

An algorithm for multiple object tracking

Background for multiple object tracking -- Data association -- The model of object

Occlusion reasoning for multiple object visual tracking

Thesis (Ph.D.)--Boston UniversityOcclusion reasoning for visual object tracking in uncontrolled environments is a challenging problem. It becomes significantly more difficult when dense groups of indistinguishable objects are present in the scene that cause frequent inter-object interactions and occlusions. We present several practical solutions that tackle the inter-object occlusions for video surveillance applications. In particular, this thesis proposes three methods. First, we propose "reconstruction-tracking," an online multi-camera spatial-temporal data association method for tracking large groups of objects imaged with low resolution. As a variant of the well-known Multiple-Hypothesis-Tracker, our approach localizes the positions of objects in 3D space with possibly occluded observations from multiple camera views and performs temporal data association in 3D. Second, we develop "track linking," a class of offline batch processing algorithms for long-term occlusions, where the decision has to be made based on the observations from the entire tracking sequence. We construct a graph representation to characterize occlusion events and propose an efficient graph-based/combinatorial algorithm to resolve occlusions. Third, we propose a novel Bayesian framework where detection and data association are combined into a single module and solved jointly. Almost all traditional tracking systems address the detection and data association tasks separately in sequential order. Such a design implies that the output of the detector has to be reliable in order to make the data association work. Our framework takes advantage of the often complementary nature of the two subproblems, which not only avoids the error propagation issue from which traditional "detection-tracking approaches" suffer but also eschews common heuristics such as "nonmaximum suppression" of hypotheses by modeling the likelihood of the entire image. The thesis describes a substantial number of experiments, involving challenging, notably distinct simulated and real data, including infrared and visible-light data sets recorded ourselves or taken from data sets publicly available. In these videos, the number of objects ranges from a dozen to a hundred per frame in both monocular and multiple views. The experiments demonstrate that our approaches achieve results comparable to those of state-of-the-art approaches

Traffic Surveillance and Automated Data Extraction from Aerial Video Using Computer Vision, Artificial Intelligence, and Probabilistic Approaches

In transportation engineering, sufficient, reliable, and diverse traffic data is necessary for effective planning, operations, research, and professional practice. Using aerial imagery to achieve traffic surveillance and collect traffic data is one of the feasible ways that is facilitated by the advances of technologies in many related areas. A great deal of aerial imagery datasets are currently available and more datasets are collected every day for various applications. It will be beneficial to make full and efficient use of the attribute rich imagery as a resource for valid and useful traffic data for many applications in transportation research and practice. In this dissertation, a traffic surveillance system that can collect valid and useful traffic data using quality-limited aerial imagery datasets with diverse characteristics is developed. Two novel approaches, which can achieve robust and accurate performance, are proposed and implemented for this system. The first one is a computer vision-based approach, which uses convolutional neural network (CNN) to detect vehicles in aerial imagery and uses features to track those detections. This approach is capable of detecting and tracking vehicles in the aerial imagery datasets with a very limited quality. Experimental results indicate the performance of this approach is very promising and it can achieve accurate measurements for macroscopic traffic data and is also potential for reliable microscopic traffic data. The second approach is a multiple hypothesis tracking (MHT) approach with innovative kinematics and appearance models (KAM). The implemented MHT module is designed to cooperate with the CNN module in order to extend and improve the vehicle tracking system. Experiments are designed based on a meticulously established synthetic vehicle detection datasets, originally induced scale-agonistic property of MHT, and comprehensively identified metrics for performance evaluation. The experimental results not only indicate that the performance of this approach can be very promising, but also provide solutions for some long-standing problems and reveal the impacts of frame rate, detection noise, and traffic configurations as well as the effects of vehicle appearance information on the performance. The experimental results of both approaches prove the feasibility of traffic surveillance and data collection by detecting and tracking vehicles in aerial video, and indicate the direction of further research as well as solutions to achieve satisfactory performance with existing aerial imagery datasets that have very limited quality and frame rates. This traffic surveillance system has the potential to be transformational in how large area traffic data is collected in the future. Such a system will be capable of achieving wide area traffic surveillance and extracting valid and useful traffic data from wide area aerial video captured with a single platfor

Intelligent Automatic Interpretation of Active Marine Sonar

This dissertation explores the problems raised by the design and construction of a real-time sonar interpreter operating in a three dimensional marine context, and then focusses on two major research issues inherent in sonar interpretation: the treatment of observer and object motion, and the efficient exploitation of the specularity of acoustic reflection. The theoretical results derived in these areas have been tested where appropriate by computer simulation. In the context of mobile marine robotics, the registration of sensory data obtained from differing viewpoints is of paramount importance. Small marine vehicles of the type considered here do not carry sophisticated navigational equipment, and cannot be held stationary in the water for any length of time. The viewpoint registration problem is defined and analysed in terms of the new problem of motion resolution: the task of resolving the apparent motion of objects into that part due to the movement of the observer and that due to the objects' proper motion. Two solutions to this under constrained problem are presented. The first presupposes that the observer orientation is known ~ priori so that only the translational observer motion must be determined. It is applicable to two and three-dimensional situations. The second solution determines both the translational and the rotational motion of the observer, but is restricted to a two-dimensional situation. Both solutions are based on target extensively tested in two tracking techniques, and have dimensions by computer simulation. been The necessary extensions to deal with full three-dimensional motion are also discussed. The second major research issue addressed in this thesis is the efficient use of specularity. Specular echoes have a high intrinsic information content because of the alignment conditions necessary for their generation. In the marine acoustic context they provide a significant proportion of the information available from an acoustic ranger. I suggest a new method that uses directly the information present in specular reflections and the history of the vehicle motion to classify the specular echo sources and infer the local structure of the objects bearing them. The method builds on the output of a motion resolution system. Six distinct types of specular echo source are described and three properties useful for their discrimination are discussed. A suitable inference system for the analysis and classification of specular echo sources is also proposed

Multiple human tracking in RGB-depth data: A survey

© The Institution of Engineering and Technology. Multiple human tracking (MHT) is a fundamental task in many computer vision applications. Appearance-based approaches, primarily formulated on RGB data, are constrained and affected by problems arising from occlusions and/or illumination variations. In recent years, the arrival of cheap RGB-depth devices has led to many new approaches to MHT, and many of these integrate colour and depth cues to improve each and every stage of the process. In this survey, the authors present the common processing pipeline of these methods and review their methodology based (a) on how they implement this pipeline and (b) on what role depth plays within each stage of it. They identify and introduce existing, publicly available, benchmark datasets and software resources that fuse colour and depth data for MHT. Finally, they present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets

A Map-matching Algorithm to Improve Vehicle Tracking Systems Accuracy

The satellite-based vehicle tracking systems accuracy can be improved by augmenting the positional information using road network data, in a process known as map-niatcliing. Map-matching algorithms attempt to estimate vehicle route and location in it particular road map (or any restricting track such as rails, etc), in spite of the digital map errors and GPS inaccuracies. Point-to-curve map-matching is not fully suitable to the problems since it ignores any historical data and often gives inaccurate, unstable, jumping results. The better curve-to-curve matching approach consider the road connectivity and measure the curve similarity between the track and the possible road path (hypotheses), but mostly does not have any way to manage multiple route hypotheses which have varying degree of similarity over time. The thesis presents a new distance metric for curve-to-curve mapmatching technique, integrated with a framework algorithm which is able to maintain many possible route hypotheses and pick the most likely hypothesis at a time, enabling future corrections if necessary, therefore providing intelligent guesses with considerable accuracy. A simulator is developed as a test bed for the proposed algorithm for various scenarios, including the field experiment using Garmin e-Trex GPS Receiver. The results showed that the proposed algoritlimi is able to improve the neap-matching accuracy as compared to the point-to-curve algorithm. Keywords: map-matching, vehicle tracking systems, Multiple Hypotheses Technique, Global Positioning System