Probabilistic Latent Sequential Motifs: Discovering temporal activity patterns in video scenes

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, documents are represented as a mixture of sequential activity motifs (or topics) and their starting occurrences. The novelties are threefold. First, unlike previous approaches where topics only modeled the co-occurrence of words at a given time instant, our topics model the co-occurrence and temporal order in which the words occur within a temporal window. Second, our model counts for the important case where activities occur concurrently in the document. And third, our method explicitly models with latent variables the starting time of the activities within the documents, enabling to implicitly align the occurrences of the same pattern during the joint inference of the temporal topics and their starting times. The model and its robustness to the presence of noise have been validated on synthetic data. Its effectiveness is also illustrated in video activity analysis from low-level motion features, where the discovered topics capture frequent patterns that implicitly represent typical trajectories of scene objects

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word×time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequential activity patterns (our motifs) where the mixing weights are defined by the motif starting time occurrences. The novelties are multi fold. First, unlike previous approaches where topics modeled only the co-occurrence of words at a given time instant, our motifs model the co-occurrence and temporal order in which the words occur within a temporal window. Second, unlike traditional Dynamic Bayesian Networks (DBN), our model accounts for the important case where activities occur concurrently in the video (but not necessarily in syn- chrony), i.e., the advent of activity motifs can overlap. The learning of the motifs in these difficult situations is made possible thanks to the introduction of latent variables representing the activity starting times, enabling us to implicitly align the occurrences of the same pattern during the joint inference of the motifs and their starting times. As a third novelty, we propose a general method that favors the recovery of sparse distributions, a highly desirable property in many topic model applications, by adding simple regularization constraints on the searched distributions to the data likelihood optimization criteria. We substantiate our claims with experiments on synthetic data to demonstrate the algorithm behavior, and on four video datasets with significant variations in their activity content obtained from static cameras. We observe that using low-level motion features from videos, our algorithm is able to capture sequential patterns that implicitly represent typical trajectories of scene objects

Time-Sensitive Topic Models for Action Recognition in Videos

In this paper, we postulate that temporal information is important for action recognition in videos. Keeping temporal information, videos are represented as word×time documents. We propose to use time-sensitive probabilistic topic models and we extend them for the con-text of supervised learning. Our time-sensitive approach is com-pared to both PLSA and Bag-of-Words. Our approach is shown to both capture semantics from data and yield classification perfor-mance comparable to other methods, outperforming them when the amount of training data is low. 1

Multi-camera Open Space Human Activity Discovery for Anomaly Detection

We address the discovery of typical activities in video stream contents and its exploitation for estimating the abnormality levels of these streams. Such estimates can be used to select the most interesting cameras to show to a human operator. Our contributions come from the following facets: i) the method is fully unsupervised and learns the activities from long term data; ii) the method is scalable and can efficiently handle the information provided by multiple un-calibrated cameras, jointly learning activities shared by them if it happens to be the case (e.g. when they have overlapping fields of view); iii) unlike previous methods which were mainly applied to structured urban traffic scenes, we show that ours performs well on videos from a metro environment where human activities are only loosely constrained

Ein Neues Verfahren zur Messung der Bakteriziden Fähigkeit des Vollblutes

Das oben erwahnte Verfahren hat vor den anderen Methoden besonders die Vorzuge, 1) da&#946; man dadurch zu einem sicheren Resultat gelangen und gleichzeitig auch jedes Datum mit exakten Ziffern zum Ausdruck bringen kann, 2) da&#946; bei diesem Verfahren keineswegs erforderlich ist, eine bestimmte Anzahl von Keimen einschlie&#946;ende Bakterienaufschwemmung herzusteHen und auch Kontrollversuch anzustellen, 3) da&#946; es von den Fehlern des Mischverhaltnisses zwischen der Bakterienlosung und dem Blut nicht so erheblich beeinflu&#946;t wird, und 4) da&#946; man durch dieses Verfahren gleichzeitig mehrere bakterientotende Faktoren untersuchen kann. Ferner hat dieses Verfahren auch den Vorzug, da&#946; es praktisch sehr einfach auszufuhren ist und nur 6 Stunden nach der Blutentnahme bereits das Ergebnis liefert. Es gestattet ferner, die bakterizide Kraft des Blutes gleichzeitig bei 6 - 8 Menschen zu untersuchen, was mich zur Uberzeugung fuhrt, da&#946; es in der Klinik hochgeschatzt werden wird. Auch das Verfahren und die ebenfalls vom mir aufgestellte Formel zur zusammenfassenden Beurteilung kann man nach meinem Erachten durch entsprechende Veranderungen einiger Faktoren ohne jede Schwierigkeiten auch fur andere Bakterienarten anwenden. Man wird wohl gegen eine einzige Lucke dieses Verfahrens, da&#946; die mikroskopische Untersuchung und die Berechnung allzu verwickelt zu sein scheint, Einwand erheben, eine Lucke, zu deren Schlu&#946; jedoch nur eine kurzfristige Ubung erfordert wird, durch welche die mikroskopische Untersuchung innerhalb 30 Minuten, die Berechnung nur in 5 Minuten vollendet werden kann. (Zur Berechnung bedarf es einer Gauss'schen Logarithmentafel.) Obgleich das geschilderte Verfahren noch viele, genauere Prufungen erheischende Punkte in sich einschlie&#946;t, mu&#946; es hier, wenn auch in Grundzugen, jetzt schon angefuhrt werden,. da ich der festen Uberzeugung bin, da&#946; es im Vergleich zu den bisherigen Methoden ein dem wirklichen Wert der Bakterizidie des Vollblutes im lebenden Organismus viel naheres Resultat liefert.</p

Environment - Application - Adaptation: a Community Architecture for Ambient Intelligence

This article considers the software problems of reuse, interoperability and evolution in the context of Ambient Intelligence. A novel approach is introduced: the Environment, Application, Adaptation (EAA) is streamlined for Ambient Intelligence and is evolved from state of the art methods used in software engineering and architecture. In the proposed approach, applications are written by using some abstract functionalities. All environment capabilities are exposed as individual services. Bridging the gap between capabilities of the environment and functionalities required by the applications is done by an adaptation layer that can be dynamically enriched and controlled by the end user. With an implementation and some examples, the approach is shown to favor development of reusable services and to enable unmodified applications to use originally unknown services

Autonomic Computer Vision Systems

Abstract. For most real applications of computer vision, variations in operating conditions result in poor reliability. As a result, real world applications tend to require lengthy set-up and frequent intervention by qualified specialists. In this paper we describe how autonomic computing can be used to reduce the cost of installation and enhance reliability for practical computer vision systems. We begin by reviewing the origins of autonomic computing. We then describe the design of a tracking-based software component for computer vision. We use a software component model to describe techniques for regulation of internal parameters, error detection and recovery, self-configuration and self-repair for vision systems