1,361 research outputs found
Visitor-art interaction by motion path detection
This paper describes a method for video-based motion path detection which is applied in the creation of an interactive artwork. The proposed algorithm, based on the Hough transform, detects parametric motion trajectories in real-time (10 fps). In order to detect people's motion under non-static background object occlusion we have also developed a video segmentation technique. The proposed interaction system adopts top-down camera view to extract spatiotemporal motion trajectories and discern predefined patterns of movement thus enabling the creation of new artistic choreographies. We present test results that illustrate the effectiveness of our method and discuss the practical applicability of our approach in other domains
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Human detection, tracking and segmentation from low-level to high-level vision
The goal of this research is to detect, segment and track a human body as well as estimate its limb configuration from cluttered background. These are fundamental research issues that have attracted intensive attention in the computer vision community because of their wide applications. Meanwhile they also remain to be ones of the most challenging research issues largely due to the ubiquitous visual ambiguities in images/videos. The other challenging factor is the ill-posed nature of the problems. Inspired by the recent findings in cognitive psychology, we adopt several biologically plausible approaches to attack these challenging problems. This dissertation provides a comprehensive study of human detection, tracking and segmentation that covers several research issues ranging from low, middle, and high-level vision.In low-level vision, we investigate video segmentation where the main challenge is the non-convex classification problem, and we develop a cascaded multi-layer segmentation framework where no-convex classification problems are addressed in a split-and-merge paradigm by combining merits of both statistical modeling and graph theory.In middle-level vision, we propose a segmentation based hypothesis-and-test paradigm to achieve joint localization and segmentation that exploits the complementary nature of region-based and edge-based shape priors. In addition, we integrate both priors into a Graph-cut framework to improve the segmentation results.In high-level vision, our research has two related parts. First, we propose a hybrid body representation that embraces part-whole shape priors and part-based spatial prior for integrated pose recognition, localization and segmentation in a given image. Second, we further combine spatial and temporal priors in an integrated online learning and inference framework, where body parts can be detected, localized and segmented simultaneously from a video sequence. Both of them are supported by previous low-level and mid-level vision tasks.Experimental results show that the proposed algorithms can achieve accurate and robust tracking, localization and segmentation results for different walking subjects with significant appearance and motion variability and under cluttered background
Object tracking using variational optic flow methods
We propose an algorithm for tracking of objects in video sequences by computing a spatiotemporal optical flow field, based on the method of Brox et al., and the application of a spatiotemporal watershed segmentation algorithm with region merging on the previously obtained vector field.Es wird ein Algorithmus zum Verfolgen von Objekten in Videosequenzen durch die Berechnung eines zeitlich-raÌumlichen optischen Flussfeldes prĂ€sentiert, basierend auf der Methode von Brox et al., und der darauffolgenden Anwendung eines zeitlich-raÌumlichen Wasserscheiden-Segmentierungsalgorithmus mit Region Merging auf dem durch den opti- schen Fluss erhaltenen Vektorfeld
Object tracking using variational optic flow methods
We propose an algorithm for tracking of objects in video sequences by computing a spatiotemporal optical flow field, based on the method of Brox et al., and the application of a spatiotemporal watershed segmentation algorithm with region merging on the previously obtained vector field.Es wird ein Algorithmus zum Verfolgen von Objekten in Videosequenzen durch die Berechnung eines zeitlich-raÌumlichen optischen Flussfeldes prĂ€sentiert, basierend auf der Methode von Brox et al., und der darauffolgenden Anwendung eines zeitlich-raÌumlichen Wasserscheiden-Segmentierungsalgorithmus mit Region Merging auf dem durch den opti- schen Fluss erhaltenen Vektorfeld
Automated detection and analysis of fluorescence changes evoked by molecular signalling
Fluorescent dyes and genetically encoded fluorescence indicators (GEFI) are common tools for visualizing concentration changes of specific ions and messenger molecules during intra- as well as intercellular communication. While fluorescent dyes have to be directly loaded into target cells and function only transiently, the expression of GEFIs can be controlled in a cell and time-specific fashion, even allowing long-term analysis in living organisms. Dye and GEFI based fluorescence fluctuations, recorded using advanced imaging technologies, are the foundation for the analysis of physiological molecular signaling. Analyzing the plethora of complex fluorescence signals is a laborious and time-consuming task. An automated analysis of fluorescent signals circumvents user bias and time constraints. However, it requires to overcome several challenges, including correct estimation of fluorescence fluctuations at basal concentrations of messenger molecules, detection and extraction of events themselves, proper segmentation of neighboring events as well as tracking of propagating events. Moreover, event detection algorithms need to be sensitive enough to accurately capture localized and low amplitude events exhibiting a limited spatial extent.
This thesis presents three novel algorithms, PBasE, CoRoDe and KalEve, for the automated analysis of fluorescence events, developed to overcome the aforementioned challenges. The algorithms are integrated into a graphical application called MSparkles, specifically designed for the analysis of fluorescence signals, developed in MATLAB. The capabilities of the algorithms are demonstrated by analyzing astroglial Ca2+ events, recorded in anesthetized and awake mice, visualized using genetically encoded Ca2+ indicators (GECIs) GCaMP3 as well as GCaMP5. The results were compared to those obtained by other software packages. In addition, the analysis of neuronal Na+ events recorded in acute brain slices using SBFI-AM serve to indicate the putatively broad application range of the presented algorithms. Finally, due to increasing evidence of the pivotal role of astrocytes in neurodegenerative diseases such as epilepsy, a metric to assess the synchronous occurrence of fluorescence events is introduced. In a proof-of-principle analysis, this metric is used to correlate astroglial Ca2+ events with EEG measurementsFluoreszenzfarbstoffe und genetisch kodierte Fluoreszenzindikatoren (GEFI) sind gĂ€ngige Werkzeuge zur Visualisierung von KonzentrationsĂ€nderungen bestimmter Ionen und BotenmolekĂŒle der intra- sowie interzellulĂ€ren Kommunikation. WĂ€hrend Fluoreszenzfarbstoffe direkt in die Zielzellen eingebracht werden mĂŒssen und nur ĂŒber einen begrenzten Zeitraum funktionieren, kann die Expression von GEFIs zell- und zeitspezifisch gesteuert werden, was darĂŒber hinaus Langzeitanalysen in lebenden Organismen ermöglicht. Farbstoff- und GEFI-basierte Fluoreszenzfluktuationen, die mit Hilfe moderner bildgebender Verfahren aufgezeichnet werden, bilden die Grundlage fĂŒr die Analyse physiologischer molekularer Kommunikation. Die Analyse einer groĂen Zahl komplexer Fluoreszenzsignale ist jedoch eine schwierige und zeitaufwĂ€ndige Aufgabe. Eine automatisierte Analyse ist dagegen weniger zeitaufwĂ€ndig und unabhĂ€ngig von der Voreingenommenheit des Anwenders. Allerdings mĂŒssen hierzu mehrere Herausforderungen bewĂ€ltigt werden. Unter anderem die korrekte SchĂ€tzung von Fluoreszenzschwankungen bei Basalkonzentrationen von BotenmolekĂŒlen, die Detektion und Extraktion von Signalen selbst, die korrekte Segmentierung benachbarter Signale sowie die Verfolgung sich ausbreitender Signale. DarĂŒber hinaus mĂŒssen die Algorithmen zur Signalerkennung empfindlich genug sein, um lokalisierte Signale mit geringer Amplitude sowie begrenzter rĂ€umlicher Ausdehnung genau zu erfassen.
In dieser Arbeit werden drei neue Algorithmen, PBasE, CoRoDe und KalEve, fĂŒr die automatische Extraktion und Analyse von Fluoreszenzsignalen vorgestellt, die entwickelt wurden, um die oben genannten Herausforderungen zu bewĂ€ltigen. Die Algorithmen sind in eine grafische Anwendung namens MSparkles integriert, die speziell fĂŒr die Analyse von Fluoreszenzsignalen entwickelt und in MATLAB implementiert wurde. Die FĂ€higkeiten der Algorithmen werden anhand der Analyse astroglialer Ca2+-Signale demonstriert, die in narkotisierten sowie wachen MĂ€usen aufgezeichnet und mit den genetisch kodierten Ca2+-Indikatoren (GECIs) GCaMP3 und GCaMP5 visualisiert wurden. Erlangte Ergebnisse werden anschlieĂend mit denen anderer Softwarepakete verglichen. DarĂŒber hinaus dient die Analyse neuronaler Na+-Signale, die in akuten Hirnschnitten mit SBFI-AM aufgezeichnet wurden, dazu, den breiten Anwendungsbereich der Algorithmen aufzuzeigen. Zu guter Letzt wird aufgrund der zunehmenden Indizien auf die zentrale Rolle von Astrozyten bei neurodegenerativen Erkrankungen wie Epilepsie eine Metrik zur Bewertung des synchronen Auftretens fluoreszenter Signale eingefĂŒhrt. In einer Proof-of-Principle-Analyse wird diese Metrik verwendet, um astrogliale Ca2+-Signale mit EEG-Messungen zu korrelieren
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MAC-REALM: A video content feature extraction and modelling framework
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A consequence of the âdata delugeâ is the exponential increase in digital video footage, while the ability to find relevant video clips diminishes. Traditional text based search engines are no longer optimal for searching, as they cannot provide a granular search of the content inside video footage. To be able to search the video in a content based manner, the content features of the video need to be extracted and modelled into a content model, which can then act as a searchable proxy for the video content. This thesis focuses on the extraction of syntactic and semantic content features and content modelling, using machine driven processes, with either little or no user interaction. Our abstract framework design extracts syntactic and semantic content features and compiles them into an integrated content model. The framework integrates a four plane strategy that consists of a pre-processing plane that removes redundant data and filters the media to improve the feature extraction properties of the media; a syntactic feature extraction plane that extracts low level syntactic feature and mid-level syntactic features that have semantic attributes; a semantic relationship analysis and linkage plane, where the spatial and temporal relationships of all the content features are defined, and finally a content modelling stage where the syntactic and semantic content features are integrated into a content model. Each of the four planes can be split into three layers namely, the content layer, where the content to be processed is stored; the application layer, where the content is converted into content descriptions, and the MPEG-7 layer, where content descriptions are serialised. Using MPEG-7 standards to produce the content model will provide wide-ranging interoperability, while facilitating granular multi-content type searches. The framework is aiming to âbridgeâ the semantic gap, by integrating the syntactic and semantic content features from extraction through to modelling. The design of the framework has been implemented into a prototype called MAC-REALM, which has been tested and evaluated for its effectiveness to extract and model content features. Conclusions are drawn about the research output as a whole and whether they have met the objectives. Finally, future work is presented on how concept detection and crowd sourcing can be used with MAC-REALM
Hierarchical Visual Content Modelling and Query based on Trees
In recent years, such vast archives of video information have become available that human annotation of content is no longer feasible; automation of video content analysis is therefore highly desirable. The recognition of semantic content in images is a problem that relies on prior knowledge and learnt information and that, to date, has only been partially solved. Salient analysis, on the other hand, is statistically based and highlights regions that are distinct from their surroundings, while also being scalable and repeatable. The arrangement of salient information into hierarchical tree structures in the spatial and temporal domains forms an important step to bridge the semantic salient gap.
Salient regions are identified using region analysis, rank ordered and documented in a tree for further analysis. A structure of this kind contains all the information in the original video and forms an intermediary between video processing and video understanding, transforming video analysis to a syntactic database analysis problem.
This contribution demonstrates the formulation of spatio-temporal salient trees the syntax to index them, and provides an interface for higher level cognition in machine vision
Video surveillance systems-current status and future trends
Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance
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