1,720,970 research outputs found

    Exploiting sparse representations for robust analysis of noisy complex video scenes

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    Abstract. Recent works have shown that, even with simple low level visual cues, complex behaviors can be extracted automatically from crowded scenes, e.g. those depicting public spaces recorded from video surveillance cameras. However, low level features as optical flow or fore-ground pixels are inherently noisy. In this paper we propose a novel unsupervised learning approach for the analysis of complex scenes which is specifically tailored to cope directly with features ’ noise and uncer-tainty. We formalize the task of extracting activity patterns as a matrix factorization problem, considering as reconstruction function the robust Earth Mover’s Distance. A constraint of sparsity on the computed basis matrix is imposed, filtering out noise and leading to the identification of the most relevant elementary activities in a typical high level behavior. We further derive an alternate optimization approach to solve the pro-posed problem efficiently and we show that it is reduced to a sequence of linear programs. Finally, we propose to use short trajectory snippets to account for object motion information, in alternative to the noisy optical flow vectors used in previous works. Experimental results demonstrate that our method yields similar or superior performance to state-of-the arts approaches.

    Space Speaks - Towards Socially and Personality Aware Visual Surveillance

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    There is a complex unwritten code which regulates human interactions. In this paper we present a camera based monitoring system that explores the relationship between proxemics, visual attention and personality traits during interaction. People’s relative positions an head poses are extracted with a multi-target tracking algorithm and used to (i) estimate, under the ’thin slices’ hypothesis, the level of extroversion and neuroticism of a person, to (ii) learn a model of people interactive behavior that improves, once integrated in the algorithm, the accuracy of the tracking estimates, and to (iii) steer a set of active cameras to the subject found to exhibit the most peculiar interaction pattern. We report on experimental results in a natural scenario where people engage in a party

    A Prototype Learning Framework using EMD: Application to Complex Scenes Analysis

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    In the last decades, many efforts have been devoted to develop methods for automatic scene understanding in the context of video surveillance applications. This paper presents a novel non-object centric approach for complex scene analysis. Similarly to previous methods, we use low-level cues to individuate atomic activities and create clip histograms. Differently from recent works, the task of discovering high-level activity patterns is formulated as a convex prototype learning problem. This problem results into a simple linear program that can be solved efficiently with standard solvers. The main advantage of our approach is that, using as objective function the Earth Mover's Distance (EMD), the similarity among elementary activities is taken into account in the learning phase. To improve scalability we also consider some variants of EMD adopting L1 as ground distance for one and two dimensional, linear and circular histograms. As in these cases only the similarity between neighboring atomic activities is taken into account, we also propose an automatic strategy for sorting them. Experimental results on publicly available datasets show that our method compares favorably with state-of-the-art approaches, often outperforming them

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed

    Variations on the Author

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    “Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship

    Appropriate Similarity Measures for Author Cocitation Analysis

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    We provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis

    Dispelling the Myths Behind First-author Citation Counts

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    We conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more sophisticated methods
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