An index for moving objects with constant-time access to their compressed trajectories

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Geographical Information Science in 2021, available at: https://doi.org/10.1080/13658816.2020.1833015Versión final aceptada de: Nieves R. Brisaboa, Travis Gagie, Adrián Gómez-Brandón, Gonzalo Navarro & José R. Paramá (2021) An index for moving objects with constant-time access to their compressed trajectories, International Journal of Geographical Information Science, 35:7, 1392-1424, DOI: 10.1080/13658816.2020.1833015[Abstract]: As the number of vehicles and devices equipped with GPS technology has grown explosively, an urgent need has arisen for time- and space-efficient data structures to represent their trajectories. The most commonly desired queries are the following: queries about an object’s trajectory, range queries, and nearest neighbor queries. In this paper, we consider that the objects can move freely and we present a new compressed data structure for storing their trajectories, based on a combination of logs and snapshots, with the logs storing sequences of the objects’ relative movements and the snapshots storing their absolute positions sampled at regular time intervals. We call our data structure ContaCT because it provides Constant- time access to Compressed Trajectories. Its logs are based on a compact partial-sums data structure that returns cumulative displacement in constant time, and allows us to compute in constant time any object’s position at any instant, enabling a speedup when processing several other queries. We have compared ContaCT experimentally with another compact data structure for trajectories, called GraCT, and with a classic spatio-temporal index, the MVR-tree. Our results show that ContaCT outperforms the MVR-tree by orders of magnitude in space and also outperforms the compressed representation in time performance.This work was supported by Xunta de Galicia/FEDER-UE under Grants [IN848D-2017-2350417; IN852A 2018/14; ED431C 2017/58]; Xunta de Galicia and European Union (European Regional Development Fund- Galicia 2014-2020 Program) with the support of CITIC research center under Grant [ED431G 2019/01]; Ministerio de Ciencia, Innovación y Universidades under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7]; A.G. was supported by Ministerio de Educación y Formación Profesional (FPU) [grant number FPU16/02914]; G.N. was supported by ANID - Millennium Science Initiative Program under Grant [ICN17_002]; and Fondecyt under Grant [1-200038]. T.G. was supported by NSERC under grant [RGPIN-2020-07185].Xunta de Galicia; IN848D-2017-2350417Xunta de Galicia; IN852A 2018/14Xunta de Galicia; ED431C 2017/58Xunta de Galicia; ED431G 2019/0

NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA

Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002

Detecting and indexing moving objects for Behavior Analysis by Video and Audio Interpretation

2012 - 2013In the last decades we have assisted to a growing need for security in many public environments. According to a study recently conducted by the European Security Observatory, one half of the entire population is worried about the crime and requires the law enforcement to be protected. This consideration has lead the proliferation of cameras and microphones, which represent a suitable solution for their relative low cost of maintenance, the possibility of installing them virtually everywhere and, finally, the capability of analysing more complex events. However, the main limitation of this traditional audiovideo surveillance systems lies in the so called psychological overcharge issue of the human operators responsible for security, that causes a decrease in their capabilities to analyse raw data flows from multiple sources of multimedia information; indeed, as stated by a study conducted by Security Solutions magazine, after 12 minutes of continuous video monitoring, a guard will often miss up to 45% of screen activity. After 22 minutes of video, up to 95% is overlooked. For the above mentioned reasons, it would be really useful to have available an intelligent surveillance system, able to provide images and video with a semantic interpretation, for trying to bridge the gap between their low-level representation in terms of pixels, and the high-level, natural language description that a human would give about them. On the other hand, this kind of systems, able to automatically understand the events occurring in a scene, would be really useful in other application fields, mainly oriented to marketing purposes. Especially in the last years, a lot of business intelligent applications have been installed for assisting decision makers and for giving an organization’s employees, partners and suppliers easy access to the information they need to effectively do their jobs... [edited by author]XII n.s

Knowledge discovery from trajectories

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAs a newly proliferating study area, knowledge discovery from trajectories has attracted more and more researchers from different background. However, there is, until now, no theoretical framework for researchers gaining a systematic view of the researches going on. The complexity of spatial and temporal information along with their combination is producing numerous spatio-temporal patterns. In addition, it is very probable that a pattern may have different definition and mining methodology for researchers from different background, such as Geographic Information Science, Data Mining, Database, and Computational Geometry. How to systematically define these patterns, so that the whole community can make better use of previous research? This paper is trying to tackle with this challenge by three steps. First, the input trajectory data is classified; second, taxonomy of spatio-temporal patterns is developed from data mining point of view; lastly, the spatio-temporal patterns appeared on the previous publications are discussed and put into the theoretical framework. In this way, researchers can easily find needed methodology to mining specific pattern in this framework; also the algorithms needing to be developed can be identified for further research. Under the guidance of this framework, an application to a real data set from Starkey Project is performed. Two questions are answers by applying data mining algorithms. First is where the elks would like to stay in the whole range, and the second is whether there are corridors among these regions of interest

Patterns of motion in non-overlapping networks using vehicle tracking data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 121-131).We present a systematic framework to learn motion patterns based on vehicle tracking data captured by multiple non-overlapping uncalibrated cameras. We assume that the tracks from individual cameras are available. We define the key problems related to the multi-camera surveillance system and present solutions to these problems: learning the topology of the network, constructing tracking correspondences between different views, learning the activity clusters over global views and finally detecting abnormal events. First, we present a weighted cross correlation model to learn the topology of the network without solving correspondence in the first place. We use estimates of normalized color and apparent size to measure similarity of object appearance between different views. This information is used to temporally correlated observations, allowing us to infer possible links between disjoint views, and to estimate the associated transition time. Based on the learned cross correlation coefficient, the network topology can be fully recovered. Then, we present a MAP framework to match two objects along their tracks from non overlapping camera views and discuss how the learned topology can reduce the correspondence search space dramatically. We propose to learn the color transformation in [iota][alpha][beta] space to compensate for the varying illumination conditions across different views, and learn the inter-camera time transition and the shape/size transformation between different views.(cont.) After we model the correspondence probability for observations captured by different source/sinks, we adopt a probabilistic framework to use this correspondence probability in a principled manner. Tracks are assigned by estimating the correspondences which maximize the posterior probabilities (MAP) using the Hungarian algorithm. After establishing the correspondence, we have a set of stitched trajectories, in which elements from each camera can be combined with observations in multiple subsequent cameras generated by the same object. Finally, we show how to learn the activity clusters and detect abnormal activities using the mixture of unigram model with the stitched trajectories as input. We adopt a bag - of - words presentation, and present a Bayesian probabilistic approach in which trajectories are represented by a mixture model. This model can classify trajectories into different activity clusters, and gives representations of both new trajectories and abnormal trajectories.by Chaowei Niu.Ph.D

Anomaly Detection and Prediction of Human Actions in a Video Surveillance Environment

World wide focus has over the years been shifting towards security issues, not in least due to recent world wide terrorist activities. Several researchers have proposed state of the art surveillance systems to help with some of the security issues with varying success. Recent studies have suggested that the ability of these surveillance systems to learn common environmental behaviour patterns as wells as to detect and predict unusual, or anomalous, activities based on those learnt patterns are possible improvements to those systems. In addition, some of these surveillance systems are still run by human operators, who are prone to mistakes and may need some help from the surveillance systems themselves in detection of anomalous activities. This dissertation attempts to address these suggestions by combining the fields of Image Understanding and Artificial Intelligence, specifically Bayesian Networks, to develop a prototype video surveillance system that can learn common environmental behaviour patterns, thus being able to detect and predict anomalous activity in the environment based on those learnt patterns. In addition, this dissertation aims to show how the prototype system can adapt to these anomalous behaviours and integrate them into its common patterns over a prolonged occurrence period. The prototype video surveillance system showed good performance and ability to detect, predict and integrate anomalous activity in the evaluation tests that were performed using a volunteer in an experimental indoor environment. In addition, the prototype system performed quite well on the PETS 2002 dataset 1, which it was not designed for. The evaluation procedure used some of the evaluation metrics commonly used on the PETS datasets. Hence, the prototype system provides a good approach to anomaly detection and prediction using Bayesian Networks trained on common environmental activities

Event Detection and Modelling for Security Application

PhD thesisThis thesis focuses on the design and implementation of a novel security domain surveillance system framework that incorporates multimodal information sources to assist the task of event detection from video and social media sources. The comprehensive framework consists of four modules including Data Source, Content Extraction, Parsing and Semantic Knowledge. The security domain ontology conceptual model is proposed for event representation and tailored in conformity with elementary aspects of event description. The adaptation of DOLCE foundational ontology promotes flexibility for heterogeneous ontologies to interoperate. The proposed mapping method using eXtensible Stylesheet Language Transformation (XSLT) stylesheet approach is presented to allow ontology enrichment and instance population to be executed efficiently. The dataset for visual semantic analysis utilizes video footage of 2011 London Riots obtained from Scotland Yard. The concepts person, face, police, car, fire, running, kicking and throwing are chosen to be analysed. The visual semantic analysis results demonstrate successful persons, actions and events detection in the video footage of riot events. For social semantic analysis, a collection of tweets from twitter channels that was actively reporting during the 2011 London Riots was compiled to create a Twitter corpus. The annotated data are mapped in the ontology based on six concepts: token, location, organization, sentence, verb, and noun. Several keywords related to the event that has been presented in the visual and social media sources are chosen to examine the correlation between both sources and to draw supplementary information regarding the event. The chosen keywords describe actions running, throwing, and kicking; activity attack, smash and loot; event fire; and location Hackney and Croydon. An experiment in respect to concept-noun relations are also been executed. The ontology-based visual and social media analysis yields a promising result in analysing long content surveillance videos and lengthy text corpus of social media user-generated content. Adopting ontology-based approach, the proposed novel security domain surveillance system framework enables a large amount of visual and social media data to be analysed systematically and automatically, and promotes a better method for event detection and understanding