Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

Surveillance camera calibration from observations of a pedestrian

Calibrated cameras are an extremely useful resource for computer vision scenarios. Typically, cameras are calibrated through calibration targets, measurements of the observed scene, or self-calibrated through features matched between cameras with overlapping fields of view. This paper considers an approach to camera calibration based on observations of a pedestrian and compares the resulting calibration to a commonly used approach requiring that measurements be made of the scene.

Video activity extraction and reporting with incremental unsupervised learning

The present work presents a new method for activity extraction and reporting from video based on the aggregation of fuzzy relations. Trajectory clustering is first employed mainly to discover the points of entry and exit of mobiles appearing in the scene. In a second step, proximity relations between resulting clusters of detected mobiles and contextual elements from the scene are modeled employing fuzzy relations. These can then be aggregated employing typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the structure of the scene and characterises the ongoing different activities of the scene. Discovered activity zones can be reported as activity maps with different granularities thanks to the analysis of the transitive closure matrix. Taking advantage of the soft relation properties, activity zones and related activities can be labeled in a more human-like language. We present results obtained on real videos corresponding to apron monitoring in the Toulouse airport in France.

The strength of the system and my experiences of it, was that in fact, oddly enough, it was actually a very collaborative enterprise already

Video:35 mins, 2006. The video shows a group of performers in a studio and seminar situation. Individually addressing the camera they offer personal views and experiences of their own art production in relation to the institution, while reflecting on their role as teachers. The performance scripts mainly originate from a series of real interviews with a diverse group of artist teachers, who emphasise the collaborative, performative and subversive nature of teaching. These views may seems symptomatic for contemporary art practices, but are ultimately antagonistic to the ongoing commodification of the system of art education.

Modeling camera orientation and 3D structure from a sequence of images taken by a perambulating commercial video camera

In this paper we report the degree of reliability of image sequences taken by off-the-shelf TV cameras for modeling camera rotation and reconstructing 3D structure using computer vision techniques. This is done in spite of the fact that computer vision systems usually use imaging devices that are specifically designed for the human vision. Our scenario consists of a static scene and a mobile camera moving through the scene. The scene is any long axial building dominated by features along the three principal orientations and with at least one wall containing prominent repetitive planar features such as doors, windows bricks etc. The camera is an ordinary commercial camcorder moving along the axial axis of the scene and is allowed to rotate freely within the range +/- 10 degrees in all directions. This makes it possible that the camera be held by a walking unprofessional cameraman with normal gait, or to be mounted on a mobile robot. The system has been tested successfully on sequence of images of a variety of structured, but fairly cluttered scenes taken by different walking cameramen. The potential application areas of the system include medicine, robotics and photogrammetry.

Visual surveillance for moving vehicles

An overview is given of a vision system for locating, recognising and tracking multiple vehicles, using an image sequence taken by a single camera mounted on a moving vehicle. The camera motion is estimated by matching features on the ground plane from one image to the next. Vehicle detection and hypothesis generation are performed using template correlation and a 3D wire frame model of the vehicle is fitted to the image. Once detected and identified, vehicles are tracked using dynamic filtering. A separate batch mode filter obtains the 3D trajectories of nearby vehicles over an extended time. Results are shown for a motorway image sequence.

Visual surveillance for moving vehicles

An overview is given of a novel vision system for locating, recognising and tracking multiple vehicles.

A specific pole placement technique to minimise hardware requirements within a real time terrestrial and cable video echo canceller

The real time hardware architecture of a deterministic video echo canceller (deghoster) system is presented. The deghoster is capable of calculating all the multipath channel distortion characteristics from terrestrial and cable television in one single pass while performing real time video in-line ghost cancellation. The results from the actual system are also presented in this paper.

The findings from a single pass deghoster prototype for terrestrial transmitted video

A deterministic prototype video deghoster is presented which is capable of calculating all the multipath channel distortion characteristics in one single pass and subsequently removing the multipath distortions, commonly termed ghosts. Within the system, a channel identification algorithm finds in isolation all the ghost components while a dedicated DSP filter subsystem is capable of removing ghosts in real time. The results from the system are presented.

An eye for an eye: robust motion segmentation by applying the latest in human vision research

Automatically extracting interesting objects from videos is a very challenging task and is applicable to many research areas such robotics, medical imaging, content based indexing and visual surveillance. Automated visual surveillance is a major research area in computational vision and a commonly applied technique in an attempt to extract objects of interest is that of motion segmentation. Motion segmentation relies on the temporal changes that occur in video sequences to detect objects, but as a technique it presents many challenges that researchers have yet to surmount. Changes in real-time video sequences not only include interesting objects, environmental conditions such as wind, cloud cover, rain and snow may be present, in addition to rapid lighting changes, poor footage quality, moving shadows and reflections. The list provides only a sample of the challenges present. This thesis explores the use of motion segmentation as part of a computational vision system and provides solutions for a practical, generic approach with robust performance, using current neuro-biological, physiological and psychological research in primate vision as inspiration.

Tracking performance evaluation on PETS 2015 Challenge datasets

This paper presents a quantitative evaluation of a tracking system on PETS 2015 Challenge datasets using well-established performance measures. Using the existing tools, the tracking system implements an end-to-end pipeline that include object detection, tracking and post- processing stages. The evaluation results are presented on the provided sequences of both ARENA and P5 datasets of PETS 2015 Challenge. The results show an encouraging performance of the tracker in terms of accuracy but a greater tendency of being prone to cardinality error and ID changes on both datasets. Moreover, the analysis show a better performance of the tracker on visible imagery than on thermal imagery.

Vehicle subtype, make and model classification from side profile video

This paper addresses the challenging domain of vehicle classification from pole-mounted roadway cameras, specifically from side-profile views. A new public vehicle dataset is made available consisting of over 10000 side profile images (86 make/model and 9 sub-type classes). 5 state-of-the-art classifiers are applied to the dataset, with the best achieving high classification rates of 98.7% for sub-type and 99.7- 99.9% for make and model recognition, confirming the assertion made that single vehicle side profile images can be used for robust classification.

Meeting detection in video through semantic analysis

In this paper we present a novel approach to detect people meeting. The proposed approach works by translating people behaviour from trajectory information into semantic terms. Having available a semantic model of the meeting behaviour, the event detection is performed in the semantic domain. The model is learnt employing a soft-computing clustering algorithm that combines trajectory information and motion semantic terms. A stable representation can be obtained from a series of examples. Results obtained on a series of videos with different types of meeting situations show that the proposed approach can learn a generic model that can effectively be applied on the behaviour recognition of meeting situations.

Surveillance for adverse events after DTwP/Hib vaccination in Brazil: Sensitivity and factors associated with reporting

We estimated the sensitivity, i.e., the proportion of all cases of adverse events following immunization (AEFIs) reported to the Brazilian passive surveillance for adverse events following immunization (PSAEFI) with the diphtheria-tetanus-whole-cell pertussis-Haemophilus influenzae type b (DTwP/Hib) vaccine, as well as investigating factors associated with AEFIs reporting. During 2003-2004, 8303 AEFIs associated with DTwP-Hib were reported; hypotonic-hyporesponsive episodes (HHEs), fever and convulsions being the most common. Cure without sequel was achieved in 98.4% of the cases. The mean sensitivity of the PSAEFI was 22.3% and 31.6%, respectively, for HHE and convulsions, varying widely among states. Reporting rates correlated positively with the Human Development Index and coverage of adequate prenatal care, correlating negatively with infant mortality rates. Quality of life indicators and the degree of organization of health services are associated with greater PSAEFI sensitivity. In addition to consistently describing the principal AEFIs, PSAEFI showed the DTwP/Hib vaccine to be safe and allayed public fears related to its use. (C) 2010 Elsevier Ltd. All rights reserved.

Dynamics of Aeromonas species isolated from wastewater treatment system

Aeromonas are widely distributed in the aquatic environment, and are considered to be emerging organisms that can produce a series of virulence factors. The present study was carried out in a sanitary sewage stabilization pond treatment system, located in Lins, State of Sao Paulo, Brazil. Most probable number was applied for estimation of the genus Aeromonas. Colony isolation was carried out on blood agar ampicillin and confirmed by biochemical characterization. Aeromonas species were isolated in 72.4% of influent samples, and in 55.2 and 48.3% of effluent from anaerobic and facultative lagoons, respectively. Thirteen Aeromonas species were isolated, representing most of the recognized species of these organisms. Even though it was possible to observe a tendency of decrease, total elimination of these organisms from the studied system was not achieved. Understanding of the pathogenic organism`s dynamics in wastewater treatment systems with a reuse potential is especially important because of the risk it represents.