High resolution surveillance video cameras are invaluable resources for effective crime prevention and forensic investigations. However, increasing communication bandwidth requirements of high definition surveillance videos are severely limiting the number of cameras that can be deployed. Higher bitrate also increases operating expenses due to higher data communication and storage costs. Hence, it is essential to develop low complexity algorithms which reduce data rate of the compressed video stream without affecting the image fidelity. In this thesis, a computer vision aided H.264 surveillance video encoder and four associated algorithms are proposed to reduce the bitrate. The proposed techniques are (I) Speeded up foreground segmentation, (II) Skip decision, (III) Reference frame selection and (IV) Face Region-of-Interest (ROI) coding. In the first part of the thesis, a modification to the adaptive Gaussian Mixture Model (GMM) based foreground segmentation algorithm is proposed to reduce computational complexity. This is achieved by replacing expensive floating point computations with low cost integer operations. To maintain accuracy, we compute periodic floating point updates for the GMM weight parameter using the value of an integer counter. Experiments show speedups in the range of 1.33 - 1.44 on standard video datasets where a large fraction of pixels are multimodal. In the second part, we propose a skip decision technique that uses a spatial sampler to sample pixels. The sampled pixels are segmented using the speeded up GMM algorithm. The storage pattern of the GMM parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. In the third part, a reference frame selection algorithm is proposed to maximize the number of background Macroblocks (MB’s) (i.e. MB’s that contain background image content) in the Decoded Picture Buffer. This reduces the cost of coding uncovered background regions. Distortion over foreground pixels is measured to quantify the performance of skip decision and reference frame selection techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence. In the final part of the thesis, face and shadow region detection is combined with the skip decision algorithm to perform ROI coding for pedestrian surveillance videos. Since person identification requires high quality face images, MB’s containing face image content are encoded with a low Quantization Parameter setting (i.e. high quality). Other regions of the body in the image are considered as RORI (Regions of reduced interest) and are encoded at low quality. The shadow regions are marked as Skip. Techniques that use only facial features to detect faces (e.g. Viola Jones face detector) are not robust in real world scenarios. Hence, we propose to initially detect pedestrians using deformable part models. The face region is determined using the deformed part locations. Detected pedestrians are tracked using an optical flow based tracker combined with a Kalman filter. The tracker improves the accuracy and also avoids the need to run the object detector on already detected pedestrians. Shadow and skin detector scores are computed over super pixels. Bilattice based logic inference is used to combine multiple likelihood scores and classify the super pixels as ROI, RORI or RONI. The coding mode and QP values of the MB’s are determined using the super pixel labels. The proposed techniques provide a further reduction in bitrate of up to 50.2%.

As user experience can be a competitive edge, it lies in the interest of businesses to be aware of how users perceive the services they provide. For telecommunications operators, how network performance influences user experience is critical. To attain this knowledge, one can survey users. However, sometimes users are not available or willing to answer. For this reason, there exists an interest in estimating the quality of user experience without having to ask users directly. Previous research has studied how the relationship between network performance and the quality of experience can be modelled over time through a fixed window classification approach. This work aims to extend this research by investigating the applicability of a regression approach without the fixed window limitation by the application of an Long Short Term Memmory based Machine Learning model. Aggregation of both network elements and user feedback through the application of three different clustering techniques was used to overcome challenges in user feedback sparsity. The performance while using each clustering technique was evaluated. It was found that all three methods can outperform a baseline based on the weekly average of the user feedback. The effect of applying different levels of detrending was also examined. It was shown that detrending the time series based on a smaller superset may increase overall performance but hinder relative model improvement, indicating that some helpful information may be lost in this process. The results should inspire future works to consider a regression approach for modelling Quality of Experience as a function of network performance as an avenue worthy of further study. This work should also motivate further research into the generalizability of models trained on network elements that reside in areas of different urban and rural conditions. / Användarupplevelsen kan utgöra en konkurrensfördel och således ligger det i marknadsaktörernas intressen att vara medvetna om hur användarna upplever det tjänster de erbjuder. Före telekommunikationsoperatörer är det kritiskt at vare varse om hur nätverkets prestanda influerar användarnas upplevelse. För att förskaffa sig den informationen kan operatörer välja att fråga användarna direkt. Detta kan dock vara svårt då användare kanske inte finns tillgängliga för eller inte är villiga att besvara operatörens frågor. Med detta som utgångspunkt finns det därför ett intresse för att estimera kundernas upplevelse utan att direkt fråga dem. Tidigare studier har undersökt möjligheten att genom klassificeringsmetoder som tillämpats på avgränsade tidsfönster modellera förhållandet mellan nätverksprestanda och kundupplevelse. Detta arbete syftar till att utvidga forskningsområdet genom att studera tillämparbarheten av att använda regressionsmetoder utan begränsningen av ett avgränsat tidsfönster. Detta ska göras genom att tillämpa en Long Short Term Memmory baserad maskininlärningsmodell. Genom att aggregera både nätverkselement och användarfeedback i en process som nyttjat tre olika klustringstekniker har utmaningar med glesfördelad feedback från användare hanterats. Resultaten av att använda vardera klustringsteknik har utvärderats. Från utvärderingen fans att alla tre metoder presterar bättre än ett jämförelsemått bestående av ett veckovis genomsnitt av användarnas återkoppling. Effekten av att applicera olika nivåer av aggregering för att ta bort trender i data. Resultaten visar att modellerna presenterat bättre då den övermängd som används för att ta bort trenden i en given delmängd då skillnaden mellan dessa är mindre. Dock försämrades den relative förbättringen hos modellerna då skillnaden mellan delmängd och övermängd minskade. Detta tror indikera att nyttig information i sammanhanget går förlorad i processen av att ta bort trenden i datamängden. De uppnådda resultaten bör inspirera framtida studier till att ha regressionsmodeller i åtanke när användarupplevelsen skall modelleras som en funktion av närverkets prestanda. Detta arbete borde även motivera vidare forskning kring huruvida modeller som tränats på nätverkselement belägna i urbana eller lantliga områden generaliserar till nätverks element i andra områden.

Voice recognition has become a more focused and researched field in the last century,and new techniques to identify speech has been introduced. A part of voice recognition isspeaker verification which is divided into Front-end and Back-end. The first componentis the front-end or feature extraction where techniques such as Mel-Frequency CepstrumCoefficients (MFCC) is used to extract the speaker specific features of a speech signal,MFCC is mostly used because it is based on the known variations of the humans ear’scritical frequency bandwidth. The second component is the back-end and handles thespeaker modeling. The back-end is based on the Gaussian Mixture Model (GMM) andGaussian Mixture Model-Universal Background Model (GMM-UBM) methods forenrollment and verification of the specific speaker. In addition, normalization techniquessuch as Cepstral Means Subtraction (CMS) and feature warping is also used forrobustness against noise and distortion. In this paper, we are going to build a speakerverification system and experiment with a variance in the amount of training data for thetrue speaker model, and to evaluate the system performance. And further investigate thearea of security in a speaker verification system then two methods are compared (GMMand GMM-UBM) to experiment on which is more secure depending on the amount oftraining data available.This research will therefore give a contribution to how much data is really necessary fora secure system where the False Positive is as close to zero as possible, how will theamount of training data affect the False Negative (FN), and how does this differ betweenGMM and GMM-UBM.The result shows that an increase in speaker specific training data will increase theperformance of the system. However, too much training data has been proven to beunnecessary because the performance of the system will eventually reach its highest point and in this case it was around 48 min of data, and the results also show that the GMMUBM model containing 48- to 60 minutes outperformed the GMM models.

This thesis investigates out-of-distribution recognition for time-series data of pulsedradar signals. The classifier is a naive Bayesian classifier based on Gaussian mixturemodels and Dirichlet process mixture models. In the mixture models, we model thedistribution of three pulse features in the time series, namely radio-frequency in thepulse, duration of the pulse, and pulse repetition interval which is the time betweenpulses. We found that simple thresholds on the likelihood can effectively determine ifsamples are out-of-distribution or belong to one of the classes trained on. In addition,we present a simple method that can be used for deinterleaving/pulse classification andshow that it can robustly classify 100 interleaved signals and simultaneously determineif pulses are out-of-distribution. / Det här examensarbetet undersöker hur en maskininlärnings-modell kan anpassas för attkänna igen när pulserade radar-signaler inte tillhör samma fördelning som modellen är tränadmed men också känna igen om signalen tillhör en tidigare känd klass. Klassifieringsmodellensom används här är en naiv Bayesiansk klassifierare som använder sig av Gaussian mixturemodels och Dirichlet Process mixture models. Modellen skapar en fördelning av tidsseriedatan för pulserade radar-signaler och specifikt för frekvensen av varje puls, pulsens längd och tiden till nästa puls. Genom att sätta gränser i sannolikheten av varje puls eller sannolikhetenav en sekvens kan vi känna igen om datan är okänd eller tillhör en tidigare känd klass.Vi presenterar även en enkel metod för att klassifiera specifika pulser i sammanhang närflera signaler överlappar och att metoden kan användas för att robust avgöra om pulser ärokända.

In autonomous driving, scenario generation can play a critical role when it comes to the verification of the autonomous driving software. Since uncertainty is a major component in driving, there cannot be just one right answer to a prediction for the trajectory or the behaviour, and it becomes important to account for and model that uncertainty. Several approaches have been tried for generating the future scenarios for a vehicle and one such pioneering work set out to model the behaviour of the vehicles probabilistically while tackling the challenges of representation, flexibility, and transferability within one system. The proposed system is called the Semantic Graph Network (SGN) which utilizes feedforward neural networks, Gated Recurrent Units (GRU), and a generative model called the Mixed Density Network to serve its purpose. This thesis project set out in the direction of the implementation of this research work in the context of highway merger scenario and consists of three parts. The first part involves basic data analysis for the employed dataset, whereas the second part involves a model that implements certain parts of the SGN including a variation of the context encoding and the Mixture Density Network. The third and the final part is an attempt to recreate the SGN itself. While the first and the second parts were implemented successfully, for the third part, only certain objectives could be achieved. / Vid autonom körning kan scenariegenerering spela en avgörande roll när det gäller verifieringen av programvaran för autonom körning. Eftersom osäkerhet är en viktig komponent i körning kan det inte bara finnas ett rätt svar på en förutsägelse av banan eller beteendet, och det blir viktigt att redogöra för och modellera den osäkerheten. Flera tillvägagångssätt har prövats för att generera framtidsscenarierna för ett fordon och ett sådant banbrytande arbete gick ut på att modellera fordonens beteende sannolikt samtidigt som utmaningarna med representation, flexibilitet och överförbarhet inom ett system hanteras. Det föreslagna systemet kallas Semantic Graph Network (SGN) som använder neurala nätverk, Gated Recurrent Units (GRU) och en generativ modell som kallas Mixed Density Network för att tjäna sitt syfte. Detta examensarbete riktar sig mot genomförandet av detta forskningsarbete i samband med motorvägssammanslagningsscenariot och består av tre delar. Den första delen involverar grundläggande dataanalys för den använda datamängden, medan den andra delen involverar en modell som implementerar vissa delar av SGN inklusive en variation av kontextkodningen och Mixture Density Network. Den tredje och sista delen är ett försök att återskapa själva SGN. Även om den första och den andra delen genomfördes framgångsrikt, kunde endast vissa mål uppnås för den tredje delen.

This thesis investigates the computer vision application of live fish recognition, which is needed in application scenarios where manual annotation is too expensive, when there are too many underwater videos. This system can assist ecological surveillance research, e.g. computing fish population statistics in the open sea. Some pre-processing procedures are employed to improve the recognition accuracy, and then 69 types of features are extracted. These features are a combination of colour, shape and texture properties in different parts of the fish such as tail/head/top/bottom, as well as the whole fish. Then, we present a novel Balance-Guaranteed Optimized Tree with Reject option (BGOTR) for live fish recognition. It improves the normal hierarchical method by arranging more accurate classifications at a higher level and keeping the hierarchical tree balanced. BGOTR is automatically constructed based on inter-class similarities. We apply a Gaussian Mixture Model (GMM) and Bayes rule as a reject option after the hierarchical classification to evaluate the posterior probability of being a certain species to filter less confident decisions. This novel classification-rejection method cleans up decisions and rejects unknown classes. After constructing the tree architecture, a novel trajectory voting method is used to eliminate accumulated errors during hierarchical classification and, therefore, achieves better performance. The proposed BGOTR-based hierarchical classification method is applied to recognize the 15 major species of 24150 manually labelled fish images and to detect new species in an unrestricted natural environment recorded by underwater cameras in south Taiwan sea. It achieves significant improvements compared to the state-of-the-art techniques. Furthermore, the sequence of feature selection and constructing a multi-class SVM is investigated. We propose that an Individual Feature Selection (IFS) procedure can be directly exploited to the binary One-versus-One SVMs before assembling the full multiclass SVM. The IFS method selects different subsets of features for each Oneversus- One SVM inside the multiclass classifier so that each vote is optimized to discriminate the two specific classes. The proposed IFS method is tested on four different datasets comparing the performance and time cost. Experimental results demonstrate significant improvements compared to the normal Multiclass Feature Selection (MFS) method on all datasets.

139 leaves printed single pages, preliminary pages i-xi and numbered pages 1-127. Includes bibliography and a list of figures and tables. Digitized at 600 dpi grayscale to pdf format (OCR),using a Bizhub 250 Konica Minolta Scanner. / Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2003. / ENGLISH ABSTRACT: Probability density functions (PDFs) and cumulative distribution functions (CDFs) play a central role in statistical pattern recognition and verification systems. They allow observations that do not occur according to deterministic rules to be quantified and modelled. An example of such observations would be the voice patterns of a person that is used as input to a biometric security device. In order to model such non-deterministic observations, a density function estimator is employed to estimate a PDF or CDF from sample data. Although numerous density function estimation techniques exist, all the techniques can be classified into one of two groups, parametric and non-parametric, each with its own characteristic advantages and disadvantages. In this research, we introduce a novel approach to density function estimation that attempts to combine some of the advantages of both the parametric and non-parametric estimators. This is done by considering density estimation using an abstract approach in which the density function is modelled entirely in terms of its moments or characteristic function. New density function estimation techniques are first developed in theory, after which a number of practical density function estimators are presented. Experiments are performed in which the performance of the new estimators are compared to two established estimators, namely the Parzen estimator and the Gaussian mixture model (GMM). The comparison is performed in terms of the accuracy, computational requirements and ease of use of the estimators and it is found that the new estimators does combine some of the advantages of the established estimators without the corresponding disadvantages. / AFRIKAANSE OPSOMMING: Waarskynlikheids digtheidsfunksies (WDFs) en Kumulatiewe distribusiefunksies (KDFs) speel 'n sentrale rol in statistiese patroonherkenning en verifikasie stelsels. Hulle maak dit moontlik om nie-deterministiese observasies te kwantifiseer en te modelleer. Die stempatrone van 'n spreker wat as intree tot 'n biometriese sekuriteits stelsel gegee word, is 'n voorbeeld van so 'n observasie. Ten einde sulke observasies te modelleer, word 'n digtheidsfunksie afskatter gebruik om die WDF of KDF vanaf data monsters af te skat. Alhoewel daar talryke digtheidsfunksie afskatters bestaan, kan almal in een van twee katagoriee geplaas word, parametries en nie-parametries, elk met hul eie kenmerkende voordele en nadele. Hierdie werk Ie 'n nuwe benadering tot digtheidsfunksie afskatting voor wat die voordele van beide die parametriese sowel as die nie-parametriese tegnieke probeer kombineer. Dit word gedoen deur digtheidsfunksie afskatting vanuit 'n abstrakte oogpunt te benader waar die digtheidsfunksie uitsluitlik in terme van sy momente en karakteristieke funksie gemodelleer word. Nuwe metodes word eers in teorie ondersoek en ontwikkel waarna praktiese tegnieke voorgele word. Hierdie afskatters het die vermoe om 'n wye verskeidenheid digtheidsfunksies af te skat en is nie net ontwerp om slegs sekere families van digtheidsfunksies optimaal voor te stel nie. Eksperimente is uitgevoer wat die werkverrigting van die nuwe tegnieke met twee gevestigde tegnieke, naamlik die Parzen afskatter en die Gaussiese mengsel model (GMM), te vergelyk. Die werkverrigting word gemeet in terme van akkuraatheid, vereiste numeriese verwerkingsvermoe en die gemak van gebruik. Daar word bevind dat die nuwe afskatters weI voordele van die gevestigde afskatters kombineer sonder die gepaardgaande nadele.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The importance of Language Identification for African languages is seeing a dramatic increase due to the development of telecommunication infrastructure and, as a result, an increase in volumes of data and speech traffic in public networks. By automatically processing the raw speech data the vital assistance given to people in distress can be speeded up, by referring their calls to a person knowledgeable in that language. To this effect a speech corpus was developed and various algorithms were implemented and tested on raw telephone speech data. These algorithms entailed data preparation, signal processing, and statistical analysis aimed at discriminating between languages. The statistical model of Gaussian Mixture Models (GMMs) were chosen for this research due to their ability to represent an entire language with a single stochastic model that does not require phonetic transcription. Language Identification for African languages using GMMs is feasible, although there are some few challenges like proper classification and accurate study into the relationship of langauges that need to be overcome. Other methods that make use of phonetically transcribed data need to be explored and tested with the new corpus for the research to be more rigorous. / AFRIKAANSE OPSOMMING: Die belang van die Taal identifiseer vir Afrika-tale is sien ’n dramatiese toename te danke aan die ontwikkeling van telekommunikasie-infrastruktuur en as gevolg ’n toename in volumes van data en spraak verkeer in die openbaar netwerke.Deur outomaties verwerking van die ruwe toespraak gegee die noodsaaklike hulp verleen aan mense in nood kan word vinniger-up ”, deur te verwys hul oproepe na ’n persoon ingelichte in daardie taal. Tot hierdie effek van ’n toespraak corpus het ontwikkel en die verskillende algoritmes is gemplementeer en getoets op die ruwe telefoon toespraak gegee.Hierdie algoritmes behels die data voorbereiding, seinverwerking, en statistiese analise wat gerig is op onderskei tussen tale.Die statistiese model van Gauss Mengsel Modelle (GGM) was gekies is vir hierdie navorsing as gevolg van hul vermo te verteenwoordig ’n hele taal met’ n enkele stogastiese model wat nodig nie fonetiese tanscription nie. Taal identifiseer vir die Afrikatale gebruik GGM haalbaar is, alhoewel daar enkele paar uitdagings soos behoorlike klassifikasie en akkurate ondersoek na die verhouding van TALE wat moet oorkom moet word.Ander metodes wat gebruik maak van foneties getranskribeerde data nodig om ondersoek te word en getoets word met die nuwe corpus vir die ondersoek te word strenger.

Apesar da ampla diversidade de línguas indígenas no Brasil, poucas pesquisas estudam estas línguas e suas relações. Inúmeros esforços têm sido dedicados a procurar similaridades entre as palavras das línguas indígenas e classificá-las em famílias de línguas. Seguindo a classificação mais aceita das línguas indígenas do Brasil, esta pesquisa propõe comparar palavras de 10 línguas indígenas brasileiras. Para isso, considera-se que estas palavras são sinais de fala e estima-se a função de distribuição de probabilidade (PDF) de cada palavra, usando um modelo de mistura de gaussianas (GMM). A PDF foi considerada um modelo para representar as palavras. Os modelos foram comparados utilizando medidas de distância para construir estruturas hierárquicas que evidenciaram possíveis relações entre as palavras. Seguindo esta linha, a hipótese levantada nesta pesquisa é que as PDFs baseadas em GMM conseguem caracterizar as palavras das línguas indígenas, permitindo o emprego de medidas de distância entre elas para estabelecer relações entre as palavras, de forma que tais relações confirmem algumas das classificações. Os parâmetros do GMM foram calculados utilizando o algoritmo Maximização da Expectância (em inglês, Expectation Maximization (EM)). A divergência Kullback Leibler (KL) foi empregada para medir semelhança entre as PDFs. Esta divergência serve de base para estabelecer as estruturas hierárquicas que ilustram as relações entre os modelos. A estimativa da PDF, baseada em GMM foi testada com o auxílio de sinais simulados, sendo possível confirmar que os parâmetros obtidos são próximos dos originais. Foram implementadas várias medidas de distância para avaliar se a semelhança entre os modelos estavam determinadas pelos modelos e não pelas medidas adotadas neste estudo. Os resultados de todas as medidas foram similares, somente foi observada alguma diferença nos agrupamentos realizados pela distância C2, por isso foi proposta como complemento da divergência KL. Estes resultados sugerem que as relações entre os modelos dependem das suas características, não das métricas de distância selecionadas no estudo e que as PDFs baseadas em GMM, conseguem fazer uma caracterização adequada das palavras. Em geral, foram observados agrupamentos entre palavras que pertenciam a línguas de um mesmo tronco linguístico, assim como se observou uma tendência a incluir línguas isoladas nos agrupamentos dos troncos linguísticos. Palavras que pertenciam a determinada língua apresentaram um comportamento padrão, sendo identificadas por esse tipo de comportamento. Embora os resultados para as palavras das línguas indígenas sejam inconclusivos, considera-se que o estudo foi útil para aumentar o conhecimento destas 10 línguas estudadas, propondo novas linhas de pesquisas dedicadas à análise destas palavras. / Although there exists a large diversity of indigenous languages in Brazil, there are few researches on these languages and their relationships. Numerous efforts have been dedicated to search for similarities among words of indigenous languages to classify them into families. Following the most accepted classification of Brazilian indigenous languages, this research proposes to compare words of 10 Brazilian indigenous languages. The words of the indigenous languages are considered speech signals and the Probability Distribution Function (PDF) of each word was estimated using the Gaussian Mixture Models (GMM). This estimation was considered a model to represent each word. The models were compared using distance measures to construct hierarchical structures that illustrate possible relationships among words. The hypothesis in this research is that the estimation of the PDF, based on GMM can characterize the words of indigenous languages, allowing the use of distance measures between the PDFs to establish relationships among the words and confirm some of the classifications. The Expectation Maximization algorithm (EM) was implemented to estimate the parameters that describe the GMM. The Kullback Leibler (KL) divergence was used to measure similarities between two PDFs. This divergence is the basis to establish the hierarchical structures that show the relationships among the models. The PDF estimation, based on GMM was tested using simulated signals, allowing confirming the useful approximation of the original parameters. Several distance measures were implemented to prove that the similarities among the models depended on the model of each word, and not on the distance measure adopted in this study. The results of all measures were similar, however, as the clustering results of the C2 distances showed some differences from the other clusters, C2 distance was proposed to complement the KL divergence. The results suggest that the relationships between models depend on their characteristics, and not on the distance measures selected in this study, and the PDFs based on GMM can properly characterize the words. In general, relations among languages that belong to the same linguistic branch were illustrated, showing a tendency to include isolated languages in groups of languages that belong to the same linguistic branches. As the GMM of some language families presents a standard behavior, it allows identifying each family. Although the results of the words of indigenous languages are inconclusive, this study is considered very useful to increase the knowledge of these types of languages and to propose new research lines directed to analyze this type of signals.

Aprender conceitos provenientes de fluxos de dados é uma tarefa significamente diferente do aprendizado tradicional em lote. No aprendizado em lote, existe uma premissa implicita que os conceitos a serem aprendidos são estáticos e não evoluem significamente com o tempo. Por outro lado, em fluxos de dados os conceitos a serem aprendidos podem evoluir ao longo do tempo. Esta evolução é chamada de mudança de conceito, e torna a criação de um conjunto fixo de treinamento inaplicável neste cenário. O aprendizado incremental é uma abordagem promissora para trabalhar com fluxos de dados. Contudo, na presença de mudanças de conceito, conceitos desatualizados podem causar erros na classificação de eventos. Apesar de alguns métodos incrementais baseados no modelo de misturas gaussianas terem sido propostos na literatura, nota-se que tais algoritmos não possuem uma política explicita de descarte de conceitos obsoletos. Nesse trabalho um novo algoritmo incremental para fluxos de dados com mudanças de conceito baseado no modelo de misturas gaussianas é proposto. O método proposto é comparado com vários algoritmos amplamente utilizados na literatura, e os resultados mostram que o algoritmo proposto é competitivo com os demais em vários cenários, superando-os em alguns casos. / Learning concepts from data streams differs significantly from traditional batch learning. In batch learning there is an implicit assumption that the concept to be learned is static and does not evolve significantly over time. On the other hand, in data stream learning the concepts to be learned may evolve over time. This evolution is called concept drift, and makes the creation of a fixed training set be no longer applicable. Incremental learning paradigm is a promising approach for learning in a data stream setting. However, in the presence of concept drifts, out dated concepts can cause misclassifications. Several incremental Gaussian mixture models methods have been proposed in the literature, but these algorithms lack an explicit policy to discard outdated concepts. In this work, a new incremental algorithm for data stream with concept drifts based on Gaussian Mixture Models is proposed. The proposed methodis compared to various algorithms widely used in the literature, and the results show that it is competitive with them invarious scenarios, overcoming them in some cases.