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

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Kalibrierung von Kameras mit allgemeinen, stetigen und nicht-symmetrischen Kameramodellen

In diesem Beitrag stellen wir eine neue Kalibrierungsmethode vor. Diese Methode kann potentiell nicht-symmetrische Kameras modellieren, die möglicherweise auch keinen einzelnen Brennpunkt haben. In diesem Artikel vergleichen wir dieses Verfahren mit anderen Kalibriermethoden und beschränken uns wegen der Vergleichbarkeit dabei vorerst auf symmetrische Kameras mit einem Brennpunkt. Wir verfahren klassisch: Zuerst initialisieren wir das System mit linearen Methoden. Danach wird das System nicht-linear optimiert. Wir vergleichen dazu verschiedene Möglichkeiten die sich aus dieser neuen Modellierung ergeben. Wir zeigen, dass die Methode mit den klassischen Methoden mithalten kann und sich in extremen Situationen sehr gut verhält - z.B. bei kurzbrennweitigen Optiken

Epipolar Geometry of Heterogeneous Stereo Camera Systems

Zusammenfassung: In den letzten Jahrzehnten wuchs der Markt für Kameras mit nichtperspektivischen Abbildungsverhalten beträchtlich. Insbesondere Kamerasysteme mit großem Öffnungswinkel finden zunehmend Verwendung. Analog zu klassischen, homogenen Stereosystemen, die aus zwei perspektivisch abbildenden Kameras bestehen, ist die Kombination verschiedenartiger Kamerasysteme denkbar. In diesem Artikel untersuchen wir die Epipolargeometrie bei heterogenen Stereokamerasystemen mit generischen Abbildungsmodellen. Wir präsentieren eine analytische Lösung für die Transformation eines beliebigen Bildpunktes einer Kamera in seine Epipolarrepräsentation der zweiten Kamera. Zusätzlich haben wir einen Blockmatcher implementiert, um in diesen heterogenen Systemen eine Disparitätskarte zu erstellen. Die Ergebnisse zeigen, dass die so erzeugten Disparitätskarten trotz unterschiedlicher Abbildungseigenschaften der jeweiligen Kamera relativ genau werden

Modellierung von Tierskeletten zur Ableitung von 3D-Strukturen aus Einzelbilddaten

Das Ableiten von 3D-Informationen aus Bilddaten stellt in der Regel besondere Anforderungen an die verwendete Kamerahardware. Es existieren jedoch Ansätze, die aus einem einzelnen Kamerabild 3D-Daten des Skeletts bekannter Objekte extrahieren können: Auf Basis neuronaler Netze erfolgt die Extraktion von Features, welche den Skelettpunkten zugeordnet werden können. Eine anschließende Verfeinerung erlaubt die Festlegung der Skelettpunkte und die Extraktion der 3D-Koordinaten. Voraussetzung für dieses Verfahren ist die dreidimensionale Modellierung des zu erkennenden Objektes. Hierbei können jedoch auch variable Parameter verwendet werden, welche das Anwenden auf allgemeinere Objekte erlauben. In einem konkreten Beispiel wird das Tracking von Pferdeskeletten betrachtet, um hiermit eine Verhaltensanalyse von Pferden durchführen zu können. Herausfordernd ist dabei die Bildung eines einfachen Skelettmodells, welches aber variabel genug ist, um alle relevanten Bewegungen des Pferdes darstellen zu können, gleichzeitig die Anzahl der Variablen gering hält, damit die Menge der notwendigen Trainingsdaten handhabbar und klein bleibt

Calibration and epipolar geometry of generic heterogenous camera systems

The application of perspective camera systems in photogrammetry and computer vision is state of the art. In recent years nonperspective and especially omnidirectional camera systems were increasingly used in close-range photogrammetry tasks. In general perspective camera model, i. e. pinhole model, cannot be applied when using non-perspective camera systems. However, several camera models for different omnidirectional camera systems are proposed in literature. Using different types of cameras in a heterogeneous camera system may lead to an advantageous combination. The advantages of different camera systems, e. g. field of view and resolution, result in a new enhanced camera system. If these different kinds of cameras can be modeled, using a unified camera model, the total calibration process can be simplified. Sometimes it is not possible to give the specific camera model in advance. In these cases a generic approach is helpful. Furthermore, a simple stereo reconstruction becomes possible using a fisheye and a perspective camera for example. In this paper camera models for perspective, wide-angle and omnidirectional camera systems are evaluated. The crucial initialization of the model’s parameters is conducted using a generic method that is independent of the particular camera system. The accuracy of this generic camera calibration approach is evaluated by calibration of a dozen of real camera systems. It will be shown, that a unified method of modeling, parameter approximation and calibration of interior and exterior orientation can be applied to derive 3D object data

Traffic Observation and Situation Assessment

Utilization of camera systems for surveillance tasks (e. g. traffic monitoring) has become a standard procedure and has been in use for over 20 years. However, most of the cameras are operated locally and data analyzed manually. Locally means here a limited field of view and that the image sequences are processed independently from other cameras. For the enlargement of the observation area and to avoid occlusions and non-accessible areas multiple camera systems with overlapping and non-overlapping cameras are used. The joint processing of image sequences of a multi-camera system is a scientific and technical challenge. The processing is divided traditionally into camera calibration, object detection, tracking and interpretation. The fusion of information from different cameras is carried out in the world coordinate system. To reduce the network load, a distributed processing concept can be implemented. Object detection and tracking are fundamental image processing tasks for scene evaluation. Situation assessments are based mainly on characteristic local movement patterns (e.g. directions and speed), from which trajectories are derived. It is possible to recognize atypical movement patterns of each detected object by comparing local properties of the trajectories. Interaction of different objects can also be predicted with an additional classification algorithm. This presentation discusses trajectory based recognition algorithms for atypical event detection in multi object scenes to obtain area based types of information (e.g. maps of speed patterns, trajectory curvatures or erratic movements) and shows that two-dimensional areal data analysis of moving objects with multiple cameras offers new possibilities for situational analysis

Analysis of Motion Patterns for Pain Estimation of Horses

This paper focuses on the automated analysis of motion patterns for relating an individual's behaviour with its pain experience. Reliable, automated behaviour analysis can improve video observation considerably, i.e. by lessening the work load of human operators, decreasing human error and by increasing anonymity and privacy. Possible applications are observation of traffic, public places and public transport. A new potential application is the early detection of pathologically relevant events, e.g. animal diseases (e.g. colics in horses), the automated post-surgical pain assessment of animals and similar applications. The challenge in the horses scenario is that they are flight animals that can not afford much of visible pain behaviour. Our approach is built on top of state of the art methods for object detection and tracking. From the object motion we derive motion patterns and respective features which we analyse by machine-learning methods. In the this paper we will present atypical behaviour detection (i.e. pain estimation) in animal videos, for which we have acquired a large video database. It could be shown that the condition of the horse can be analysed and classified by means of local histograms