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Author |
Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez |
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Title |
Selective Spatio-Temporal Interest Points |
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Journal Article |
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Year |
2012 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
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116 |
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3 |
Pages |
396-410 |
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Recent progress in the field of human action recognition points towards the use of Spatio-TemporalInterestPoints (STIPs) for local descriptor-based recognition strategies. In this paper, we present a novel approach for robust and selective STIP detection, by applying surround suppression combined with local and temporal constraints. This new method is significantly different from existing STIP detection techniques and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-video words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on popular benchmark datasets (KTH and Weizmann), more challenging datasets of complex scenes with background clutter and camera motion (CVC and CMU), movie and YouTube video clips (Hollywood 2 and YouTube), and complex scenes with multiple actors (MSR I and Multi-KTH), validates our approach and show state-of-the-art performance. Due to the unavailability of ground truth action annotation data for the Multi-KTH dataset, we introduce an actor specific spatio-temporal clustering of STIPs to address the problem of automatic action annotation of multiple simultaneous actors. Additionally, we perform cross-data action recognition by training on source datasets (KTH and Weizmann) and testing on completely different and more challenging target datasets (CVC, CMU, MSR I and Multi-KTH). This documents the robustness of our proposed approach in the realistic scenario, using separate training and test datasets, which in general has been a shortcoming in the performance evaluation of human action recognition techniques. |
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Elsevier |
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1077-3142 |
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Admin @ si @ CHM2012 |
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1806 |
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Author |
Jordi Gonzalez; Thomas B. Moeslund; Liang Wang |
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Title |
Semantic Understanding of Human Behaviors in Image Sequences: From video-surveillance to video-hermeneutics |
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Journal Article |
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Year |
2012 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
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Volume |
116 |
Issue |
3 |
Pages |
305–306 |
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Purpose: Atheromatic plaque progression is affected, among others phenomena, by biomechanical, biochemical, and physiological factors. In this paper, the authors introduce a novel framework able to provide both morphological (vessel radius, plaque thickness, and type) and biomechanical (wall shear stress and Von Mises stress) indices of coronary arteries.Methods: First, the approach reconstructs the three-dimensional morphology of the vessel from intravascular ultrasound (IVUS) and Angiographic sequences, requiring minimal user interaction. Then, a computational pipeline allows to automatically assess fluid-dynamic and mechanical indices. Ten coronary arteries are analyzed illustrating the capabilities of the tool and confirming previous technical and clinical observations.Results: The relations between the arterial indices obtained by IVUS measurement and simulations have been quantitatively analyzed along the whole surface of the artery, extending the analysis of the coronary arteries shown in previous state of the art studies. Additionally, for the first time in the literature, the framework allows the computation of the membrane stresses using a simplified mechanical model of the arterial wall.Conclusions: Circumferentially (within a given frame), statistical analysis shows an inverse relation between the wall shear stress and the plaque thickness. At the global level (comparing a frame within the entire vessel), it is observed that heavy plaque accumulations are in general calcified and are located in the areas of the vessel having high wall shear stress. Finally, in their experiments the inverse proportionality between fluid and structural stresses is observed. |
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1077-3142 |
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Admin @ si @ GMW2012 |
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2005 |
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Bhaskar Chakraborty; Jordi Gonzalez; Xavier Roca |
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Title |
Large scale continuous visual event recognition using max-margin Hough transformation framework |
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2013 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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117 |
Issue |
10 |
Pages |
1356–1368 |
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In this paper we propose a novel method for continuous visual event recognition (CVER) on a large scale video dataset using max-margin Hough transformation framework. Due to high scalability, diverse real environmental state and wide scene variability direct application of action recognition/detection methods such as spatio-temporal interest point (STIP)-local feature based technique, on the whole dataset is practically infeasible. To address this problem, we apply a motion region extraction technique which is based on motion segmentation and region clustering to identify possible candidate “event of interest” as a preprocessing step. On these candidate regions a STIP detector is applied and local motion features are computed. For activity representation we use generalized Hough transform framework where each feature point casts a weighted vote for possible activity class centre. A max-margin frame work is applied to learn the feature codebook weight. For activity detection, peaks in the Hough voting space are taken into account and initial event hypothesis is generated using the spatio-temporal information of the participating STIPs. For event recognition a verification Support Vector Machine is used. An extensive evaluation on benchmark large scale video surveillance dataset (VIRAT) and as well on a small scale benchmark dataset (MSR) shows that the proposed method is applicable on a wide range of continuous visual event recognition applications having extremely challenging conditions. |
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1077-3142 |
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ISE |
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Admin @ si @ CGR2013 |
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2413 |
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Author |
Ariel Amato; Mikhail Mozerov; Andrew Bagdanov; Jordi Gonzalez |
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Title |
Accurate Moving Cast Shadow Suppression Based on Local Color Constancy detection |
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Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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20 |
Issue |
10 |
Pages |
2954 - 2966 |
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This paper describes a novel framework for detection and suppression of properly shadowed regions for most possible scenarios occurring in real video sequences. Our approach requires no prior knowledge about the scene, nor is it restricted to specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene, the values of the background image are divided by values of the current frame in the RGB color space. We show how this luminance ratio can be used to identify segments with low gradient constancy, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of our method compared with the most sophisticated, state-of-the-art shadow detection algorithms. These results show that our approach is robust and accurate over a broad range of shadow types and challenging video conditions. |
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1057-7149 |
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ISE |
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Admin @ si @ AMB2011 |
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1716 |
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Author |
Arjan Gijsenij; Theo Gevers; Joost Van de Weijer |
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Title |
Computational Color Constancy: Survey and Experiments |
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Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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20 |
Issue |
9 |
Pages |
2475-2489 |
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Keywords |
computational color constancy;computer vision application;gamut-based method;learning-based method;static method;colour vision;computer vision;image colour analysis;learning (artificial intelligence);lighting |
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Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets. |
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1057-7149 |
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ISE;CIC |
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Admin @ si @ GGW2011 |
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1717 |
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