On-line new event detection and tracking in a multi-resource environment

Cataloged from PDF version of article.As the amount of electronically available information resources increase, the need for information also increases. Today, it is almost impossible for a person to keep track all the information resources and find new events as soon as possible. In this thesis, we present an on-line new event detection and tracking system, which automatically detects new events from multiple news resources and immediately start tracking events as they evolve. Since we implemented the on-line version of event detection approach, the novelty decision about a news story is done before processing the next one. We also present a new threshold, called support threshold, used in detection process to decrease the number of new event alarms, that are caused by informative and one-time-only news. The support threshold can be used to tune the weights of news resources. We implemented the tracking phase as an unsupervised learning process, that is, detected events are automatically tracked by training the system using the first news story of an event. Since events evolve over time, an unsupervised adaptation is used to retrain the tracking system in order to increase the tracking system performance. Adaptation is achieved by adding predicted documents to the training process. From the corpus observations, we conclude that one news story can be associated to more than one event. For this reason, the tracking system can relate a news story to more than one event. The on-line new event detection and tracking system has been tested on the Reuters news feed, available on the Internet. The Reuters news feed, that we used, comprises four independent news resources. The news stories are in Turkish.Kurt, HakanM.S

On-line new event detection and clustering using the concepts of the cover coefficient-based clustering methodology

Cataloged from PDF version of article.In this study, we use the concepts of the cover coefficient-based clustering methodology (C3 M) for on-line new event detection and event clustering. The main idea of the study is to use the seed selection process of the C3 M algorithm for the purpose of detecting new events. Since C3 M works in a retrospective manner, we modify the algorithm to work in an on-line environment. Furthermore, in order to prevent producing oversized event clusters, and to give equal chance to all documents to be the seed of a new event, we employ the window size concept. Since we desire to control the number of seed documents, we introduce a threshold concept to the event clustering algorithm. We also use the threshold concept, with a little modification, in the on-line event detection. In the experiments we use TDT1 corpus, which is also used in the original topic detection and tracking study. In event clustering and event detection, we use both binary and weighted versions of TDT1 corpus. With the binary implementation, we obtain better results. When we compare our on-line event detection results to the results of UMASS approach, we obtain better performance in terms of false alarm rates.Vural, AhmetM.S

An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations

A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increase owing to the use of both ON and OFF events. AER data acquired by a DVS are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition.The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation

Recreating the OSIRIS-REx Slingshot Manoeuvre from a Network of Ground-Based Sensors

Optical tracking systems typically trade-off between astrometric precision and field-of-view. In this work, we showcase a networked approach to optical tracking using very wide field-of-view imagers that have relatively low astrometric precision on the scheduled OSIRIS-REx slingshot manoeuvre around Earth on September 22nd, 2017. As part of a trajectory designed to get OSIRIS-REx to NEO 101955 Bennu, this flyby event was viewed from 13 remote sensors spread across Australia and New Zealand to promote triangulatable observations. Each observatory in this portable network was constructed to be as lightweight and portable as possible, with hardware based off the successful design of the Desert Fireball Network. Over a 4 hour collection window, we gathered 15,439 images of the night sky in the predicted direction of the OSIRIS-REx spacecraft. Using a specially developed streak detection and orbit determination data pipeline, we detected 2,090 line-of-sight observations. Our fitted orbit was determined to be within about 10~km of orbital telemetry along the observed 109,262~km length of OSIRIS-REx trajectory, and thus demonstrating the impressive capability of a networked approach to SSA

N-gram Overlap in Automatic Detection of Document Derivation

Establishing authenticity and independence of documents in relation to others is not a new problem, but in the era of hyper production of e-text it certainly gained even more importance. There is an increased need for automatic methods for determining originality of documents in a digital environment. The method of n-gram overlap is only one of several methods proposed by the literature and is used in a variety of systems for automatic identification of text reuse. Although the aforementioned method is quite trivial, determining the length of n-grams that would be a good indicator of text reuse is a somewhat complex issue. We assume that the optimal length of n-grams is not the same for all languages but that it depends on the particular language properties such as morphological typology, syntactic features, etc. The aim of this study is to find the optimal length of n-grams to be used for determining document derivation in Croatian language. Among the potential areas of implementation of the results of this study, we could point out automatic detection of plagiarism in academic and student papers, citation analysis, information flow tracking and event detection in on-line texts

Independent Motion Detection with Event-driven Cameras

Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for example, is easily achieved even for very fast stimuli, as only moving objects cause brightness changes. However, cameras mounted on a moving robot are typically non-stationary and the same tracking problem becomes confounded by background clutter events due to the robot ego-motion. In this paper, we propose a method for segmenting the motion of an independently moving object for event-driven cameras. Our method detects and tracks corners in the event stream and learns the statistics of their motion as a function of the robot's joint velocities when no independently moving objects are present. During robot operation, independently moving objects are identified by discrepancies between the predicted corner velocities from ego-motion and the measured corner velocities. We validate the algorithm on data collected from the neuromorphic iCub robot. We achieve a precision of ~ 90 % and show that the method is robust to changes in speed of both the head and the target.Comment: 7 pages, 6 figure

leave a trace - A People Tracking System Meets Anomaly Detection

Video surveillance always had a negative connotation, among others because of the loss of privacy and because it may not automatically increase public safety. If it was able to detect atypical (i.e. dangerous) situations in real time, autonomously and anonymously, this could change. A prerequisite for this is a reliable automatic detection of possibly dangerous situations from video data. This is done classically by object extraction and tracking. From the derived trajectories, we then want to determine dangerous situations by detecting atypical trajectories. However, due to ethical considerations it is better to develop such a system on data without people being threatened or even harmed, plus with having them know that there is such a tracking system installed. Another important point is that these situations do not occur very often in real, public CCTV areas and may be captured properly even less. In the artistic project leave a trace the tracked objects, people in an atrium of a institutional building, become actor and thus part of the installation. Visualisation in real-time allows interaction by these actors, which in turn creates many atypical interaction situations on which we can develop our situation detection. The data set has evolved over three years and hence, is huge. In this article we describe the tracking system and several approaches for the detection of atypical trajectories

Clock synchronization by remote detection of correlated photon pairs

We present an algorithm to detect the time and frequency difference of independent clocks based on observation of time-correlated photon pairs. This enables remote coincidence identification in entanglement-based quantum key distribution schemes without dedicated coincidence hardware, pulsed sources with a timing structure or very stable reference clocks. We discuss the method for typical operating conditions, and show that the requirement in reference clock accuracy can be relaxed by about 5 orders of magnitude in comparison with previous schemes.Comment: 14 pages, 6 figure