505 Hits in 5.1 sec

Splitting Gaussians in Mixture Models

Ruben Heras Evangelio, Michael Patzold, Thomas Sikora
2012 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance  
Therefore, we define an appropriate splitting operation and the corresponding criterion for the selection of candidate modes, for the case of background subtraction.  ...  Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements  ...  ACKNOWLEDGMENT The research leading to these results has received funding from the European Community FP7 under grant agreement number 261743 (NoE VideoSense).  ... 
doi:10.1109/avss.2012.69 dblp:conf/avss/EvangelioPS12 fatcat:3dsqcp2l7jfkrkcp6unzacdioi

Moving Object Detection and Segmentation using Background Subtraction by Kalman Filter

Archita Tah, Sudipta Roy, Prasenjit Das, Anirban Mitra
2017 Indian Journal of Science and Technology  
Findings: In this paper, the precise and real-time method for moving object detection and tracking is based on reference background subtraction and use threshold value dynamically to achieve a more inclusive  ...  Methods/Statistical Analysis: This paper presents the moving object tracking using Kalman filter and reference of background generation.  ...  1 For j = min to max For i = min to max Find background Insert a split image from a video Convert it from RGB to GRAY Convert it to binary I1 Convert it to binary I2 Subtract the  ... 
doi:10.17485/ijst/2017/v10i19/95427 fatcat:l7vqmjtbffa4fn3sawuxodfl3u

Noise-Robust Speaker Recognition Combining Missing Data Techniques and Universal Background Modeling

Tobias May, Steven van de Par, Armin Kohlrausch
2012 IEEE Transactions on Audio, Speech, and Language Processing  
Since 2010, he has been affiliated with the University of Oldenburg.  ...  He is currently pursuing the Ph.D. degree at the University of Oldenburg. Since 2007, he has been with the Eindhoven University of Technology, Eindhoven, The Netherlands.  ...  Srinivasan for helpful discussions and their feedback on an earlier version of this manuscript.  ... 
doi:10.1109/tasl.2011.2158309 fatcat:2kqy6okwvraencbu5y5sv6ndo4

End-to-end Background Subtraction via a Multi-scale Spatio-temporal Model

Yizhong Yang, Tao Zhang, Jinzhao Hu, Dong Xu, Guangjun Xie
2019 IEEE Access  
To improve the robustness of background subtraction, in this paper, we propose an end-to-end multi-scale spatio-temporal (MS-ST) method which is able to extract deep features from video sequences.  ...  subtraction is an important task in computer vision. Traditional approaches usually utilize low-level visual features like color, texture, or edge to build background models.  ...  For example, GMM [6] presented a typical adaptive background modeling method that assumes that historical intensities of each pixel obey a mixture of Gaussians.  ... 
doi:10.1109/access.2019.2930319 fatcat:iftxgti5yrhipgr7qcvfv3i4ti

Automatic detection of speaker state: Lexical, prosodic, and phonetic approaches to level-of-interest and intoxication classification

William Yang Wang, Fadi Biadsy, Andrew Rosenberg, Julia Hirschberg
2013 Computer Speech and Language  
acoustic features for this task using a new multilevel multistream prediction feedback and similarity-based hierarchical fusion learning approach.  ...  In the task of LOI prediction, we propose a novel Discriminative TFIDF feature to capture important lexical information and a novel Prosodic Event detection approach using AuToBI; we combine these with  ...  The subtraction allows zero contributions from Gaussians that are not affected by the MAP adaptation; this subtraction slightly improves accuracy in our dialect recognition work .  ... 
doi:10.1016/j.csl.2012.03.004 fatcat:xmkbyv6zv5bydh76e253i2gepy

A Deep Detector Classifier (DeepDC) for moving objects segmentation and classification in video surveillance

SIRINE AMMAR, Thierry Bouwmans, Nizar Zaghden, Mahmoud Neji
2020 IET Image Processing  
Finally, they take advantage of the power of generative models, which recognise the problem of semi-supervised learning as a specific missing data imputation task in order to classify the segmented objects  ...  They evaluate the method with multiple datasets and the results confirm the effectiveness of the proposed approach, which achieves superior performance over the state-of-the-art methods having the capabilities  ...  The authors also would like to thank Yi Wang, Pierre-Marc Jodoin, and Porikli Fatih for providing us with the segmentation masks of all methods of CDnet2014 benchmark [62] to compare the different approaches  ... 
doi:10.1049/iet-ipr.2019.0769 fatcat:cpth6r4f2nbdhobv5r23bgw2ie

Acoustic Scene Classification: A Competition Review [article]

Shayan Gharib, Honain Derrar, Daisuke Niizumi, Tuukka Senttula, Janne Tommola, Toni Heittola, Tuomas Virtanen, Heikki Huttunen
2018 arXiv   pre-print
We also compare the results with a neural network baseline, and show the improvement over that.  ...  Finally, we discuss the impact of using a competition as a part of a university course, and justify its importance in the curriculum based on student feedback.  ...  Moreover, fusing the three models together (plain baseline, baseline with temporal averaging and baseline with background subtraction) improves the accuracy even further, with a net accuracy increase of  ... 
arXiv:1808.02357v1 fatcat:n5axgr3vtzg7jpjikfiodznda4

Background Modelling using a Q-Tree Based Foreground Segmentation

S Shahidha Banu, N Maheswari
2020 Scalable Computing : Practice and Experience  
The extensive experimental results and the evaluation parameters of the proposed approach with the state of art method were compared against the most recent background subtraction approaches.  ...  In this paper, we presented a renewed background modelling method for foreground segmentation.  ...  It is complicated to achieve these goals with a simple background subtraction, by design, that promotes at the pixel level for improved productivity and it is not easy to analyze significant change patterns  ... 
doi:10.12694/scpe.v21i1.1603 fatcat:pw2atkwqijgojngs2n7qfmhhk4

The IBM Speaker Recognition System: Recent Advances and Error Analysis [article]

Seyed Omid Sadjadi, Jason Pelecanos, Sriram Ganapathy
2016 arXiv   pre-print
space, the application of speaker and channel-adapted features derived from an automatic speech recognition (ASR) system for speaker recognition, and the use of a DNN acoustic model with a very large  ...  We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech.  ...  For the GMM based system, a relative improvement of 30% in EER is achieved with NDA over LDA, while for the DNN based systems with MFCCs and fMLLR features relative improvements of 25% and 21% are obtained  ... 
arXiv:1605.01635v1 fatcat:dc7q25pbx5eypdmlfix7txjenm

The IBM Speaker Recognition System: Recent Advances and Error Analysis

Seyed Omid Sadjadi, Jason W. Pelecanos, Sriram Ganapathy
2016 Interspeech 2016  
space, the application of speaker and channel-adapted features derived from an automatic speech recognition (ASR) system for speaker recognition, and the use of a DNN acoustic model with a very large  ...  We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech.  ...  It was found that, while many of the recordings are audibly acceptable, there are 76 with co-channel speech (either through cross-channel feedback or from background competing speakers), 51 with background  ... 
doi:10.21437/interspeech.2016-1159 dblp:conf/interspeech/SadjadiPG16 fatcat:vjz4ftfhivf5dc2d5tu3ack3qm

Articulated human body parts detection based on cluster background subtraction and foreground matching

Harish Bhaskar, Lyudmila Mihaylova, Simon Maskell
2013 Neurocomputing  
In this paper, a framework for human body parts tracking in video sequences using a self-adaptive cluster background subtraction (CBS) scheme is proposed based on a Gaussian mixture model (GMM) and foreground  ...  The efficiency of the designed human body parts tracking framework is illustrated over various real-world video sequences.  ...  Acknowledgements We are thankful the anonymous reviewers for their suggestions helping us to improve this work.  ... 
doi:10.1016/j.neucom.2011.12.039 fatcat:3fekmbnj25gwtmpkfln2tehmbi

Unsupervised spectral subtraction for noise-robust ASR

G. Lathoud, M. Magimai-Doss, B. Mesot, H. Bourlard
2005 IEEE Workshop on Automatic Speech Recognition and Understanding, 2005.  
The goal is to improve noise-robustness of the acoustic features.  ...  In this paper, the 2-mixture model is used in an "Unsupervised Spectral Subtraction" scheme that can be applied as a pre-processing step for any acoustic feature extraction scheme, such as MFCCs or PLP  ...  The improvement is particularly large for 0, 5, 10 and 15 dB. Discussion To conclude, after initial tests on the OGI Numbers 95 task, we "froze" the approach, tested it on the Aurora 2 task.  ... 
doi:10.1109/asru.2005.1566500 fatcat:rfyidzbcnnfm5pabf54koflbji

SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity

Pierre-Luc St-Charles, Guillaume-Alexandre Bilodeau, Robert Bergevin
2015 IEEE Transactions on Image Processing  
Despite the wide variety of methods that have been proposed for this problem, none has been able to fully address the complex nature of dynamic scenes in real surveillance tasks.  ...  Besides, instead of using manually-set, frame-wide constants to dictate model sensitivity and adaptation speed, we use pixel-level feedback loops to dynamically adjust our method's internal parameters  ...  New adaptive and more flexible variations of GMM were also proposed over the years [10] - [13] to allow dynamic numbers of components for modeling as well as to improve their convergence rate.  ... 
doi:10.1109/tip.2014.2378053 pmid:25494507 fatcat:ia7frbrckbhxhaodiqusnjduui

Unsupervised Equalization of Lombard Effect for Speech Recognition in Noisy Adverse Environments

Hynek Boril, John H L Hansen
2010 IEEE Transactions on Audio, Speech, and Language Processing  
The task is to recognize neutral/LE connected digit strings presented in different levels of background car noise and Aurora 2 noises.  ...  Recently, a set of unsupervised techniques reducing ASR sensitivity to these sources of distortion have been presented, with the main focus on equalization of Lombard effect (LE).  ...  and adaptation (see [3, 4] for overviews).  ... 
doi:10.1109/tasl.2009.2034770 fatcat:bs3fb7m2ffbnhouidsalta6lja

Improved Foreground Detection via Block-Based Classifier Cascade With Probabilistic Decision Integration

Vikas Reddy, Conrad Sanderson, Brian C. Lovell
2013 IEEE transactions on circuits and systems for video technology (Print)  
subtraction is a fundamental low-level processing task in numerous computer vision applications.  ...  We propose a block-based method capable of dealing with noise, illumination variations and dynamic backgrounds, while still obtaining smooth contours of foreground objects.  ...  ACKNOWLEDGEMENTS We thank Andres Sanin for helping with the tracking framework. We also thank Sandra Mau, Mehrtash Harandi and the anonymous reviewers for their valuable feedback.  ... 
doi:10.1109/tcsvt.2012.2203199 fatcat:6i635ubunfclplzoaadlfhskmq
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