Reporting flock patterns

Data representing moving objects is rapidly getting more available, especially in the area of wildlife GPS tracking. It is a central belief that information is hidden in large data sets in the form of interesting patterns. One of the most common spatio-temporal patterns sought after is flocks. A flock is a large enough subset of objects moving along paths close to each other for a certain pre-defined time. We give a new definition that we argue is more realistic than the previous ones, and by the use of techniques from computational geometry we present fast algorithms to detect and report flocks. The algorithms are analysed both theoretically and experimentally

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Système de vidéosurveillance et de monitoring

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Privacy preserving distributed spatio-temporal data mining

Time-stamped location information is regarded as spatio-temporal data due to its time and space dimensions and, by its nature, is highly vulnerable to misuse. Privacy issues related to collection, use and distribution of individuals’ location information are the main obstacles impeding knowledge discovery in spatio-temporal data. Suppressing identifiers from the data does not suffice since movement trajectories can easily be linked to individuals using publicly available information such as home or work addresses. Yet another solution could be employing existing privacy preserving data mining techniques. However these techniques are not suitable since time-stamped location observations of an object are not plain, independent attributes of this object. Therefore, new privacy preserving data mining techniques are required to handle spatio-temporal data specifically. In this thesis, we propose a privacy preserving data mining technique and two preprocessing steps for data mining related to privacy preservation in spatio-temporal datasets: (1) Distributed clustering, (2) Centralized anonymization and (3) Distributed anonymization. We also provide security and efficiency analysis of our algorithms which shows that under reasonable conditions, achieving privacy preservation with minimal sensitive information leakage is possible for data mining purposes

교통 패턴 분석과 비정상 탐지를 위한 온라인 추론 모델

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 최진영.In this thesis, we propose a method for modeling trajectory patterns with both regional and velocity observations through the probabilistic inference model. By embedding Gaussian models into the discrete topic model framework, our method uses continuous velocity as well as regional observations unlike existing approaches. In addition, the proposed framework combined with Hidden Markov Model can cover the temporal transition of the scene state, which is useful in checking a violation of the rule that some conflict topics (e.g. two cross-traffic patterns) should not occur at the same time. To achieve online learning even with the complexity of the proposed model, we suggest a novel learning scheme instead of collapsed Gibbs sampling. The proposed two-stage greedy learning scheme is not only efficient at reducing the search space but also accurate in a way that the accuracy of online learning becomes not worse than that of the batch learning. To validate the performance of our method, experiments were conducted on various datasets. Experimental results show that our model explains satisfactorily the trajectory patterns with respect to scene understanding, anomaly detection, and prediction.Abstract Chapter 1 Introduction 1.1 Statement of Problem 1.2 Related Works 1.2.1 Motion Pattern Analysis Using Trajectory 1.2.2 Motion Pattern Analysis Using Local Motions 1.3 Contributions 1.4 Thesis Organization Chapter 2 Preliminaries 2.1 Latent Dirichlet Allocation (LDA) 2.1.1 Probabilistic Graphical Model 2.1.2 LDA Property & Formulation 2.2 Inference of LDA 2.2.1 Collapsed Gibbs Sampling 2.2.2 Variational Inference Chapter 3 Proposed Approach 3.1 Probabilistic Inference Model 3.2 Model Learning 3.2.1 Online Trajectory Clustering 3.2.2 Spatio-Temporal Dependency of Activities 3.2.3 Velocity Learning 3.3 Anomaly Detection 3.4 Summary of the Proposed Method Chapter 4 Experiments 4.1 Result of Traffic Pattern Understanding 4.2 Applications in Anomaly Detection 4.3 Prediction Task 4.4 Comparison with Sampling Chapter 5 Conculsion 5.1 Concluding Remarks 5.2 Future Works 초록Docto

Identification and tracking of marine objects for collision risk estimation.

With the advent of modem high-speed passenger ferries and the general increase in maritime traffic, both commercial and recreational, marine safety is becoming an increasingly important issue. From lightweight catamarans and fishing trawlers to container ships and cruise liners one question remains the same. Is anything in the way? This question is addressed in this thesis. Through the use of image processing techniques applied to video sequences of maritime scenes the images are segmented into two regions, sea and object. This is achieved using statistical measures taken from the histogram data of the images. Each segmented object has a feature vector built containing information including its size and previous centroid positions. The feature vectors are used to track the identified objects across many frames. With information recorded about an object's previous motion its future motion is predicted using a least squares method. Finally a high-level rule-based algorithm is applied in order to estimate the collision risk posed by each object present in the image. The result is an image with the objects identified by the placing of a white box around them. The predicted motion is shown and the estimated collision risk posed by that object is displayed. The algorithms developed in this work have been evaluated using two previously unseen maritime image sequences. These show that the algorithms developed here can be used to estimate the collision risk posed by maritime objects

Identification and tracking of maritime objects for collision risk estimation

With the advent of modem high-speed passenger ferries and the general increase in maritime traffic, both commercial and recreational, marine safety is becoming an increasingly important issue. From lightweight catamarans and fishing trawlers to container ships and cruise liners one question remains the same. Is anything in the way? This question is addressed in this thesis. Through the use of image processing techniques applied to video sequences of maritime scenes the images are segmented into two regions, sea and object. This is achieved using statistical measures taken from the histogram data of the images. Each segmented object has a feature vector built containing information including its size and previous centroid positions. The feature vectors are used to track the identified objects across many frames. With information recorded about an object's previous motion its future motion is predicted using a least squares method. Finally a high-level rule-based algorithm is applied in order to estimate the collision risk posed by each object present in the image. The result is an image with the objects identified by the placing of a white box around them. The predicted motion is shown and the estimated collision risk posed by that object is displayed. The algorithms developed in this work have been evaluated using two previously unseen maritime image sequences. These show that the algorithms developed here can be used to estimate the collision risk posed by maritime objects.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Selbstlernende akustische Mustererkennung zur Erfassung von Bauteilfehlern im Automobil

Die Schwerpunkte dieser Promotion, die in der Volkswagen Konzernforschung in Zusammenarbeit mit dem Institut für Gerätesysteme und Schaltungstechnik der Universität Rostock angefertigt wurde, liegen in der Konzept- und Algorithmenerstellung zur automatisierten Analyse des Fahrzeuginnenraumgeräuschs mit Anwendung in der Qualitätssicherung, der Produktion und den Werkstätten. Über selbstorganisierende neuronale Karten ist das Ziel erreicht worden, zeitabhängige akustische Größen von Fahrzeuggeräuschen zu interpretieren, wodurch eine Unterscheidung von Fehlerzuständen ermöglicht wird.The main focus of this doctoral thesis at the Volkswagen Group Research and the Institute of Electronic Appliances and Circuits of University Rostock is on the development of concepts and algorithms for automatic analysis of interior vehicle sound with applications in quality control, production and dealer workshops. The main target, to calculate and analyse the parameters, whether a normal interior sound or a noticeable noise occurs, is reached by neural network algorithms. Self-Organizing Maps are used for the interpretation of acoustic patterns of two research vehicles

Learning Spatio-Temporal Patterns for Predicting Object Behaviour

Rule-based systems employed to model complex object behaviours, do not necessarily provide a realistic portrayal of true behaviour. To capture the real characteristics in a specific environment, a better model may be learnt from observation. This paper presents a novel approach to learning long-term spatio-temporal patterns of objects in image sequences, using a neural network paradigm to predict future behaviour. The results demonstrate the application of our approach to the problem of predicting animal behaviour in response to a predator