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Mixture models for optical flow computation
[chapter]
1995
DIMACS Series in Discrete Mathematics and Theoretical Computer Science
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We use a simple extension of the EM-algorithm to compute a maximum likelihood estimate for the various motion parameters. ...
Preliminary experiments indicate that this approach is computationally e cient and can provide robust estimates of the optical ow v alues in the presence of outliers and multiple motions. ...
Heel for providing the Pepsi-can image sequence and J. Bergen for providing the transparency sequence. ...
doi:10.1090/dimacs/019/14
dblp:conf/dimacs/JepsonB93
fatcat:4vw5ixgwnnfi5cii7wc7d3j72u
Mixture models for optical flow computation
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
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We use a simple extension of the EM-algorithm to compute a maximum likelihood estimate for the various motion parameters. ...
Preliminary experiments indicate that this approach is computationally e cient and can provide robust estimates of the optical ow v alues in the presence of outliers and multiple motions. ...
Heel for providing the Pepsi-can image sequence and J. Bergen for providing the transparency sequence. ...
doi:10.1109/cvpr.1993.341161
dblp:conf/cvpr/JepsonB93
fatcat:puoadjzdnffatfncfkeeagr3hm
Real-Time Detection and Tracking of Moving Object
2008
2008 Second International Symposium on Intelligent Information Technology Application
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It will prove very helpful for public safety. ...
Based on above discussion in my research work I have decided to develop intelligent framework for Real time motion detection or recognition for appropriate thing or object. ...
Here foreground used for optical flow is foreground extracted using Gaussian Mixture Modelling techniques. ...
doi:10.1109/iita.2008.428
fatcat:nb23cmneqjctjhvhvdtobo54q4
HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS
2020
Journal of Information and Communication Technology
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Human activity recognition from video scenes has become a significant area of research in the field of computer vision applications. Action recognition is one of the most challenging ...
This research received no specific grant from any funding agency in the public, commercial, or not-for profit sectors. ...
Center, Department of Electronics and Communication Engineering, Vidyavardhaka College of Engineering, Mysuru, India which is recognized by the Visvesvaraya Technological University, Belagavi, India for ...
doi:10.32890/jict2020.19.2.1
fatcat:cafcrtepljesvnidrwfjuo2foa
An Efficient Algorithm for Real Time Moving Object Detection using GMM and Optical Flow
English
2015
International Journal of Innovative Research in Computer and Communication Engineering
English
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Here for proposed work used Gaussian Mixture Model and Optical Flow technique for the moving object detection. ...
Gaussian Mixture Model is better extract the foreground object but it more time consuming and Optical Flow is faster for moving object detection but it is not more accurate so by using these two approaches ...
In fig 6 it is detect motion using Gaussian Mixture Model and Optical Flow which is our proposed method.
V. ...
doi:10.15680/ijircce.2015.0306021
fatcat:j56cjpnvffbprkn4jveymswhbu
Scene understanding by statistical modeling of motion patterns
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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We propose a mixture model representation of salient patterns of optical flow, and present an algorithm for learning these patterns from dense optical flow in a hierarchical, unsupervised fashion. ...
Using low level cues of noisy optical flow, K-means is employed to initialize a Gaussian mixture model for temporally segmented clips of video. ...
Process flow of our approach: Grouping of frames into video clips, optical flow computation, Gaussian mixture model learning by K-means, filtering of noisy Gaussian components, inter-component spatiotemporal ...
doi:10.1109/cvpr.2010.5539884
dblp:conf/cvpr/SaleemiHS10
fatcat:jfjregphlrck7f2rcwklyrhjcm
Robust foreground segmentation using improved Gaussian Mixture Model and optical flow
2012
2012 International Conference on Informatics, Electronics & Vision (ICIEV)
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In this context, Gaussian Mixture Model (GMM) background subtraction has been widely employed. ...
However, the background model estimation step is still problematic; the main difficulty is to decide which distributions of the mixture belong to the background. ...
It is obtained by computing the optical flow between consecutives frames, then a measure for uniformity of motion is applied. For optical flow computation, several algorithms exist in the literature. ...
doi:10.1109/iciev.2012.6317376
fatcat:aarehtcxezhy7lbwxdtuidunpy
Sensory Anticipation of Optical Flow in Mobile Robotics
[article]
2012
arXiv
pre-print
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The learnt model is used to anticipate the optical flow up to a given time horizon and to predict an imminent collision by using reinforcement learning. ...
A sensorimotor model is acquired online by the mobile robot using a state-of-the-art method that learns the optical flow distribution in images, both in space and time. ...
First, the active mixture components are computed from the current optical flow values for each position in the sample grid. ...
arXiv:1210.1104v1
fatcat:gl35755p4jc6dpzf6vpxeonxlm
Incremental learning of an optical flow model for sensorimotor anticipation in a mobile robot
2012
2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)
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The learnt model is used to anticipate the optical flow up to a given time horizon and to predict an imminent collision by using reinforcement learning. ...
A sensorimotor model is acquired online by the mobile robot using a state-of-the-art method that learns the optical flow distribution in images, both in space and time. ...
First, the active mixture components are computed from the current optical flow values for each position in the sample grid. ...
doi:10.1109/devlrn.2012.6400589
dblp:conf/icdl-epirob/RibesCDM12
fatcat:zknqdafpabccrak6f4t6npzywa
Histogram-based foreground object extraction for indoor and outdoor scenes
2010
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing - ICVGIP '10
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In our approach the spatial histogram of a single background image is modeled as Mixture of Gaussians and this model is updated after every few frames. ...
To mitigate the errors caused due to movement of the background objects (e.g tree leaves in outdoor scenes), we also incorporate optical flow in an efficient manner. ...
Estimating optical flow on complete images is often computationally expensive. ...
doi:10.1145/1924559.1924579
dblp:conf/icvgip/Kulkarni10
fatcat:3fsthxz5b5a47i224ltyjntryu
Vehicle Detection, Tracking and Counting Using Gaussian Mixture Model and Optical Flow
2020
Journal of Engineering Research and Reports
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The proposed approach consists of an optical flow method with a Gaussian mixture model (GMM) to obtain an absolute shape of particular moving objects which improves the detection performance of moving ...
PROPOSED METHOD The proposed method is based on the Gaussian mixture model (GMM) and optical flow. ...
Gaussian Mixture Model (GMM) Gaussian mixture model consists of a mixture of K Gaussian distribution illustrated by these parameters: mean (µ), weight (w), and variance (σ2). ...
doi:10.9734/jerr/2020/v15i217141
fatcat:iyu3u2kpqjf4phia7vgfj63ov4
Spatiotemporal Background Subtraction Using Minimum Spanning Tree and Optical Flow
[chapter]
2014
Lecture Notes in Computer Science
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modeling and subtraction is a fundamental research topic in computer vision. ...
Additionally, optical flow estimation can be used to track the foreground pixels and integrated with a temporal M -smoother to ensure temporally-consistent background subtraction. ...
The computational cost of proposed spatiotemporally-consistent background model is much higher due to the use of optical flow which is known to be slow. ...
doi:10.1007/978-3-319-10584-0_34
fatcat:kfa3ovgtubczvmxumy5wihshcq
Anomaly detection in crowded scenes
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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The model for normal crowd behavior is based on mixtures of dynamic textures and outliers under this model are labeled as anomalies. ...
Three properties are identified as important for the design of a localized video representation suitable for anomaly detection in such scenes: 1) joint modeling of appearance and dynamics of the scene, ...
of PCA models of optic flow. ...
doi:10.1109/cvpr.2010.5539872
dblp:conf/cvpr/MahadevanLBV10
fatcat:gm55i2kv4rbehpvujfqajhrxoi
Analysis of Persistent Motion Patterns Using the 3D Structure Tensor
2005
2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1
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In scenes with multiple global motion patterns, a mixture model (of these global distributions) automatically factors background motion into a set of flow fields corresponding to the different motions. ...
Capturing statistics of the spatiotemporal derivatives at each pixel can efficiently model surprisingly complicated motion patterns. ...
The covariance matrix permits deeper analysis than just computing an estimate of the optic flow at each pixel. One simple confidence measure for the optic flow estimate is S = 1 − λ 3 /λ 2 . ...
doi:10.1109/acvmot.2005.21
dblp:conf/wacv/WrightP05
fatcat:pcjyxsjlibbu7lsqtkwv6fnp3y
Modeling of flow and heat transfer in a vertical reactor for the MOCVD of zirconium-based coatings
[chapter]
1998
Simulation of Semiconductor Processes and Devices 1998
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Flow and heat transfer in a vertical reactor used for the MOCVD growth of zirconium based compounds is studied by computational modeling. ...
The verification of computational predictions is done by temperature measurements. Flow regimes are compared with respect to the flow structure and temperature distribution in the reactor. ...
Modeling approach The modeling approach consists of computing the laminar, hyposonic (low Mach number) non-isothermal flow for a gas mixture with high temperature and density gradients. ...
doi:10.1007/978-3-7091-6827-1_7
fatcat:fkabvcyb3vhnffbcsaygxpohya
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