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Michael Holte; Bhaskar Chakraborty; Jordi Gonzalez; Thomas B. Moeslund |
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Title |
A Local 3D Motion Descriptor for Multi-View Human Action Recognition from 4D Spatio-Temporal Interest Points |
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2012 |
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IEEE Journal of Selected Topics in Signal Processing |
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J-STSP |
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6 |
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5 |
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553-565 |
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In this paper, we address the problem of human action recognition in reconstructed 3-D data acquired by multi-camera systems. We contribute to this field by introducing a novel 3-D action recognition approach based on detection of 4-D (3-D space $+$ time) spatio-temporal interest points (STIPs) and local description of 3-D motion features. STIPs are detected in multi-view images and extended to 4-D using 3-D reconstructions of the actors and pixel-to-vertex correspondences of the multi-camera setup. Local 3-D motion descriptors, histogram of optical 3-D flow (HOF3D), are extracted from estimated 3-D optical flow in the neighborhood of each 4-D STIP and made view-invariant. The local HOF3D descriptors are divided using 3-D spatial pyramids to capture and improve the discrimination between arm- and leg-based actions. Based on these pyramids of HOF3D descriptors we build a bag-of-words (BoW) vocabulary of human actions, which is compressed and classified using agglomerative information bottleneck (AIB) and support vector machines (SVMs), respectively. Experiments on the publicly available i3DPost and IXMAS datasets show promising state-of-the-art results and validate the performance and view-invariance of the approach. |
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1932-4553 |
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Admin @ si @ HCG2012 |
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1994 |
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Maria Vanrell; Jordi Vitria; Xavier Roca |
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A multidimensional scaling approach to explore the behavior of a texture perception algorithm. |
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1997 |
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Machine Vision and Applications |
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9 |
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262–271 |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ VVR1997 |
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35 |
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Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca |
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Title |
Integrating Vision and Language in Social Networks for Identifying Visual Patterns of Personality Traits |
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2019 |
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International Journal of Social Science and Humanity |
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IJSSH |
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9 |
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1 |
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6-12 |
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Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. In this sense, user text interactions are widely used to sense the whys of certain social user’s demands and cultural- driven interests. However, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited. Following this trend on visual-based social analysis, we present a novel methodology based on neural networks to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So, the key contribution in this work is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between posted images and the personality estimated from their accompanying texts. Thus, the experimental results are consistent with previous cyber-psychology results based on texts, suggesting that images could also be used for personality estimation: classification results on some personality traits show that specific and characteristic visual patterns emerge, in essence representing abstract concepts. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts, and to further substitute current textual personality questionnaires by image-based ones. |
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ISE; 600.119 |
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Admin @ si @ RGG2019 |
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3414 |
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Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez |
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Title |
Logo Detection With No Priors |
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2021 |
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IEEE Access |
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ACCESS |
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9 |
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106998-107011 |
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In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors. |
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Admin @ si @ VGR2021 |
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3664 |
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Author |
Albert Ali Salah; E. Pauwels; R. Tavenard; Theo Gevers |
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Title |
T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data |
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2010 |
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Sensors |
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SENS |
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10 |
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8 |
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7496-7513 |
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sensor networks; temporal pattern extraction; T-patterns; Lempel-Ziv; Gaussian mixture model; MERL motion data |
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The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences. The temporal complexity of the T-pattern approach is prohibitive in the scenarios we consider. We remedy this with a statistical model to obtain a fast and robust algorithm to find patterns in temporal data. We test our algorithm on a recent database collected with passive infrared sensors with millions of events. |
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ALTRES;ISE |
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Admin @ si @ SPT2010 |
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1845 |
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