Browsing by Subject "Hidden Markov models"

Now showing 1 - 13 of 13

Open Access
3D human pose search using oriented cylinders
(IEEE, 2009-09-10) Pehlivan, Selen; Duygulu, Pınar
In this study, we present a representation based on a new 3D search technique for volumetric human poses which is then used to recognize actions in three dimensional video sequences. We generate a set of cylinder like 3D kernels in various sizes and orientations. These kernels are searched over 3D volumes to find high response regions. The distribution of these responses are then used to represent a 3D pose. We use the proposed representation for (i) pose retrieval using Nearest Neighbor (NN) based classification and Support Vector Machine (SVM) based classification methods, and for (ii) action recognition on a set of actions using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM) based classification methods. Evaluations on IXMAS dataset supports the effectiveness of such a robust pose representation. ©2009 IEEE.
Open Access
Compressive sensing based flame detection in infrared videos
(IEEE, 2013) Günay, Osman; Çetin, A. Enis
In this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into spatio-temporal blocks and spatial and temporal feature vectors are exctracted from these blocks. Compressive Sensing is used to exctract spatial feature vectors. Compressed measurements are obtained by multiplying the pixels in the block with the sensing matrix. A new method is also developed to generate the sensing matrix. A random vector generated according to standard Gaussian distribution is passed through a wavelet transform and the resulting matrix is used as the sensing matrix. Temporal features are obtained from the vector that is formed from the difference of mean intensity values of the frames in two neighboring blocks. Spatial feature vectors are classified using Adaboost. Temporal feature vectors are classified using hidden Markov models. To reduce the computational cost only moving and bright regions are classified and classification is performed at specified intervals instead of every frame. © 2013 IEEE.
Open Access
Contour based smoke detection in video using wavelets
(IEEE, 2006-09) Töreyin, B. Uğur; Dedeoğlu, Yiğithan; Çetin, A. Enis
This paper proposes a novel method to detect smoke in video. It is assumed the camera monitoring the scene is stationary. The smoke is semi-transparent at the early stages of a fire. Therefore edges present in image frames start loosing their sharpness and this leads to a decrease in the high frequency content of the image. The background of the scene is estimated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the current and the background images. Edges of the scene produce local extrema in the wavelet domain and a decrease in the energy content of these edges is an important indicator of smoke in the viewing range of the camera. Moreover, scene becomes grayish when there is smoke and this leads to a decrease in chrominance values of pixels. Periodic behavior in smoke boundaries is also analyzed using a Hidden Markov model (HMM) mimicking the temporal behavior of the smoke. In addition, boundary of smoke regions are represented in wavelet domain and high frequency nature of the boundaries of smoke regions is also used as a clue to model the smoke flicker. All these clues are combined to reach a final decision.
Open Access
Detection and identification of changes of hidden Markov chains: Asymptotic theory
(Springer Science and Business Media B.V., 2021-10-06) Dayanık, Savaş; Yamazaki, Kazutoshi
This paper revisits a unified framework of sequential change-point detection and hypothesis testing modeled using hidden Markov chains and develops its asymptotic theory. Given a sequence of observations whose distributions are dependent on a hidden Markov chain, the objective is to quickly detect critical events, modeled by the first time the Markov chain leaves a specific set of states, and to accurately identify the class of states that the Markov chain enters. We propose computationally tractable sequential detection and identification strategies and obtain sufficient conditions for the asymptotic optimality in two Bayesian formulations. Numerical examples are provided to confirm the asymptotic optimality. © 2021, The Author(s).
Open Access
Flame detection in video using hidden Markov models
(IEEE, 2005) Töreyin, B. Uğur; Dedeoğlu, Yiğithan; Çetin, A. Enis
This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.
Open Access
Flame detection system based on wavelet analysis of PIR sensor signals with an HMM decision mechanism
(IEEE, 2008-08) Ug̃ur Töreyin, B.; Soyer, E. Birey; Urfaliog̃lu, Onay; Çetin, A. Enis
In this paper, a flame detection system based on a pyroelectric (or passive) infrared (PIR) sensor is described. The flame detection system can be used for fire detection in large rooms. The flame flicker process of an uncontrolled fire and ordinary activity of human beings and other objects are modeled using a set of Hidden Markov Models (HMM), which are trained using the wavelet transform of the PIR sensor signal. Whenever there is an activity within the viewing range of the PIR sensor system, the sensor signal is analyzed in the wavelet domain and the wavelet signals are fed to a set of HMMs. A fire or no fire decision is made according to the HMM producing the highest probability. copyright by EURASIP.
Open Access
HMM based method for dynamic texture detection
(IEEE, 2007) Töreyin, Behçet Uğur; Çetin, A. Enis
A method for detection of dynamic textures in video is proposed. It is observed that the motion vectors of most of the dynamic textures (e.g. sea waves, swaying tree leaves and branches in the wind, etc.) exhibit random motion. On the other hand, regular motion of ordinary video objects has well-defined directions. In this paper, motion vectors of moving objects are estimated and tracked based on a minimum distance based metric. The direction of the motion vectors are then quantized to define two threestate Markov models corresponding to dynamic textures and ordinary moving objects with consistent directions. Hidden Markov Models (HMMs) are used to classify the moving objects in the final step of the algorithm.
Open Access
A hybrid SVM/HMM based system for the state detection of individual finger movements from multichannel ECoG signals
(IEEE, 2011) Onaran, İbrahim; Ince, N.F.; Çetin, A. Enis; Abosch, A.
A hybrid state detection algorithm is presented for the estimation of baseline and movement states which can be used to trigger a free paced neuroprostethic. The hybrid model was constructed by fusing a multiclass Support Vector Machine (SVM) with a Hidden Markov Model (HMM), where the internal hidden state observation probabilities were represented by the discriminative output of the SVM. The proposed method was applied to the multichannel Electrocorticogram (ECoG) recordings of BCI competition IV to identify the baseline and movement states while subjects were executing individual finger movements. The results are compared to regular Gaussian Mixture Model (GMM)-based HMM with the same number of states as SVM-based HMM structure. Our results indicate that the proposed hybrid state estimation method out-performs the standard HMM-based solution in all subjects studied with higher latency. The average latency of the hybrid decoder was approximately 290ms. © 2011 IEEE.
Open Access
Nonstationary time series prediction with Markovian switching recurrent neural networks
(2021-07) İlhan, Fatih
We investigate nonlinear prediction for nonstationary time series. In most real-life scenarios such as ﬁnance, retail, energy and economy applications, time se-ries data exhibits nonstationarity due to the temporally varying dynamics of the underlying system. This situation makes the time series prediction challenging in nonstationary environments. We introduce a novel recurrent neural network (RNN) architecture, which adaptively switches between internal regimes in a Markovian way to model the nonstationary nature of the given data. Our model, Markovian RNN employs a hidden Markov model (HMM) for regime transitions, where each regime controls hidden state transitions of the recurrent cell inde-pendently. We jointly optimize the whole network in an end-to-end fashion. We demonstrate the signiﬁcant performance gains compared to conventional methods such as Markov Switching ARIMA, RNN variants and recent statistical and deep learning-based methods through an extensive set of experiments with synthetic and real-life datasets. We also interpret the inferred parameters and regime belief values to analyze the underlying dynamics of the given sequences.
Open Access
A novel model-based method for feature extraction from protein sequences for classification
(IEEE, 2006) Saraç, Ö. S.; Atalay, V.; Çetin-Atalay, Rengül
Representation of amino-acid sequences constitutes the key point in classification of proteins into functional or structural classes. The representation should contain the biologically meaningful information hidden in the primary sequence of the protein. Conserved or similar subsequences are strong indicators of functional and structural similarity. In this study we present a feature mapping that takes into account the models of the subsequences of protein sequences. An expectation-maximization algorithm along with an HMM mixture model is used to cluster and learn the models of subsequences of a given set of proteins.
Open Access
PIR sensörleriyle alev tespiti
(IEEE, 2008-04) Töreyin, B. Uǧur; Soyer, E. Birey; Urfalıoǧlu, Onay; Çetin, A. Enis
Bu bildiride, pasif kızılberisi sensor (PIR) tabanlı bir alev tespit sistemi sunulmaktadır. Önerilen yangın tespit sistemi oda içlerinde kullanılabilir. Kontrolsuz büyüyen yangın alevlerindeki kırpışma, oda içi gündelik insan hareketleri olan yürüme ve koşma ile birlikte, PIR sensörü işaretlerinin dalgacık dönüşümü katsayılarıyla eğitilmiş bir dizi saklı Markov modeliyle modellenmiştir. Sensör sisteminin görüş alanı içerisinde bir hareket tespit edildiğinde, sensör sinyali dalgacık domeninde çözümlenmekte ve bir dizi saklı Markov modeline beslenmektedir. En yüksek olasılık değerini üreten saklı Markov modeline göre “ateş” veya "ateş değil" kararı verilmektedir.
Open Access
Ses ve video işaretlerinde saklı markof modeli tabanlı düşen kişi tespiti
(IEEE, 2006-04) Töreyin, B. Uğur; Dedeoğlu, Yiğithan; Çetin, A. Enis
Automatic detection of a falling person in video is an important problem with applications in security and safety areas including supportive home environments and CCTV surveillance systems. Human motion in video is modeled using Hidden Markov Models (HMM) in this paper. In addition, the audio track of the video is also used to distinguish a person simply sitting on a floor from a person stumbling and falling. Most video recording systems have the capability of recording audio as well and the impact sound of a falling person is also available as an additional clue. Audio channel data based decision is also reached using HMMs and fused with results of HMMs modeling the video data to reach a final decision. © 2006 IEEE.
Open Access
Wavelet based detection of moving tree branches and leaves in video
(IEEE, 2006-05) Töreyin, B. Uğur; Çetin, A. Enis
A method for detection of tree branches and leaves in video is proposed. It is observed that the motion vectors of tree branches and leaves exhibit random motion. On the other hand regular motion of green colored objects has well-defined directions. In this paper, the wavelet transform of motion vectors are computed and objects are classified according to the wavelet coefficients of motion vectors. Color information is also used to reduce the search space in a given image frame of the video. Motion trajectories of moving objects are modeled as Markovian processes and Hidden Markov Models (HMMs) are used to classify the green colored objects in the final step of the algorithm. © 2006 IEEE.