Gaussian Mixture Model

Gaussian Mixture Model Many computer related vision technology, it is critical to identify moving objects from a sequence of videos frames. In order to achieve this, background subtraction is applied which mainly identifies moving objects from each portion of video frames. Background subtraction or segmentation is a widely used technique in video surveillance, target recognitions and banks. By using the Gaussian Mixture Model background model, frame pixels are deleted from the required video to achieve the desired results. The application of background subtraction involves various factors which involve developing an algorithm which is able to detect the required object robustly, it should also be able to react to various changes like illumination, starting and stopping of moving objects. Surveillance is the monitoring of the behaviour, activities or other changing information usually of people and often ina surreptitiousmanner. Video surveillance is commonly used for event detection and human identification. But it is not easy as think to detect the event or tracking the object. There are many techniques and papers introduced by many scientists for the backend process in the video surveillance. Different automated softwares are used for the analysis of the video footage. It tracks large body movements and objects. In this report we discuss the application of mixture of Gaussian (M.O.G) which is used as a background subtraction model in order to achieve object/target recognition; this method involves calculation the mean and standard deviation of each pixel frame which will be discussed more in detailed as the report progresses. 2. Background Subtraction (Literature Review) Figure 1 Background subtraction Flow Diagram [1] According to [1], the above figure shows the basic flow diagram on how background subtraction algorithm is based. The four important steps described by [1] and which play an important role in background subtraction are described below and include: 2.1 Preprocessing Temporal or spatial smoothing is used in the early pre processing stage to eliminate device noise which can be a factor under different light intensity. Smoothing technique also includes removing various other elements like environment such as rain and snow.   In real-time systems, frame size and frame rate are commonly adopted to reduce the data processing rate. Another key factor in preprocessing technique is the data format used by the background subtraction model. Most algorithms can handle luminance intensity which is one scalar value per each pixel.   As shown in the examples below [2]: Figure 2 Image on the left shows snowing and image on the right is a resultant of smoothing effect In the figure 2, shown are two images which shows snow on the left and whereas with the application of spatial and temporal smoothing on right image results in the elimination of snow producing an more clear and effective image for background subtraction. 2.2 Background Modeling This step uses the new video frame in order to calculate and update the background model. The main aim of developing a background model is that it should be robust against environmental changes in the background, but sensitive enough to identify all moving objects of interest. 2.3 Foreground Detection In this step, it identifies the pixels in the frame. Foreground detection compares the video frame with the background model, and identify candidate foreground pixels from the frame. Commonly- used approach for foreground detection is to check whether the pixel is significantly different from the corresponding background estimate as show below: A different foreground detection scheme is normalised threshold based statistics as shown below: Where,  µd and are the mean and standard deviation of the for all spatial locations (x,y). 2.4 Data Validation Finally, this step eliminated any pixels which are not connected to the image. It involves the process of improving the foreground mask based on the information obtained from the outside background model. Most background models lack three main points: 1. Ignoring any correlation between neighbouring pixels 2. The rate of adaption may not match the moving speed of the foreground object. 3. Non-stationary pixels, from moving leavers or shadow cast by moving objects are at times mistaken for true foreground objects. 3. Background Reading There are different types of background modelling which are as follows: 3.1 Simple Frame Difference Frame difference is considered the simplest form of background subtraction. The method involves the subtraction of previous pixel frame with current pixel frame and if the value obtained from this greater than a set threshold then it is considered as the foreground. Advantages: Less complex The method can easily and quickly adapt to changes Able to distinguish the background from foreground much affectively. Disadvantages: Not Robust, object has to continuously move. Only applicable when with fixed camera. Fails to handle illumination changes. 3.2 Moving Average Filtering Referring from [3], Moving Average filtering is a method in which an estimate value of a particular background pixel value is weighted as average of the previous values. Pixels in the far part are weighted at Zero and the weights increase smoothly.  Ã‚   3.3 Online Gaussian Model In this method, we assume the background as a Gaussian distribution rather than a single value. To calculate Gaussian for 1-D is as follows[4]: Where, x is the current pixel value,  µ is the mean of each pixel and sigma is the standard deviation of the pixel. Finally, the following equations are used in online updated of Gaussian mean and variance: In the above equations[4]:  µ(t) is the mean for each pixel where ÃŽÂ ± is the learning rate, t is the time of the previous pixel and x is the current pixel value. à Ã†â€™2(t) is the standard deviation of the each pixels. The foreground decision rule applied here is when the distance between each pixel value and Gaussian model is larger than a certain threshold, it is considered as the Foreground. 3.3 GMM Gaussian Mixture Model In order to use GMM the following equations are considered: In the above equations [4]:  µ(t) is the mean for each pixel where ÃŽÂ ± is the learning rate, t is the time of the previous pixel and x is the current pixel value. à Ã†â€™2(t) is the standard deviation of the each pixels. The description and implementation of GMM model will be discussed more in detail in the later part of the report 3.4 Codebook based Technique It is a method where a quantized and clustering technique is motivated by Kohonen to construct the background model from long observation sequences, without making any parameter assumptions. For each pixels, a codebook contains one or more codewords and mixed backgrounds can be modelled by using multiple codewords. Samples at each pixel are clustered into the set of codewords based on a color distortion metric together with a brightness ratio. [4] 4.0 Gaussian Mixture Model In this report we implement the Gaussian Mixture Model for background subtraction. This background subtraction model is more robust than other models discussed in previous section. Most importantly it can handle multi-modal situations e.g. trees and sky which is more effectively filtered by the GMM model. Each pixel value is modelled by a mixture of Gaussian rather than a particular type of distribution. Based on the variance of each of the Gaussian of the mixture, we determine which Gaussians may correspond to background colors. Pixel values that do not fix the background distributions are considered foreground until there is a Gaussian that includes them with sufficient consistent evidence supporting it [5]. This method is very adaptable with lighting changes, repetitive motions and slow moving objects. This method contains 2 main significant parameters -Alpha, the learning constant and T, the proportion of the data that should be accounted for by the background The Gaussian mixture components for a pixel have normalized weights calculated from the past observations. The likelihood that a pixel has a value of Xt is assumed to be:    [5] The parameters of the mixture components are updates with the new frames. A retrieved pixel value is compared with all the components of the mixture assigned to that pixel to find out if there is a match. A match is said to happen when the retrieved pixel value is within 2.5 times standard deviation of a mixture component. The update procedure is different for the matching component and other components. The mean values and the covariance matrices are updated for only the matching component. The value formulas for the matching components are: And the weights are updated with given formula    [6] In case there is not a match between the current pixel value and the mixture components related to that pixel .the component having the smallest likelihood with respect to the current pixel value is discarded. A new Gaussian component is created in place of the discarded one, having a mean value equal to the current pixel value, and a variance equal to a predetermined constant. To retrieve the foreground the following equation is used. Where, T is the threshold value for the sum of the weights.[6] 5.0 Algorithm In order to give a better understanding of the algorithm used for background subtraction the following steps were adopted to achieve the desired results: Firstly, we compare each input pixels to the mean mu of the associated components. If the value of a pixel is close enough to a chosen components mean, then that component is considered as the matched component. In order to be a matched component, the difference between the pixel and mean must be less than compared to the components standard deviation scaled by factor D in the algorithm. Secondly, update the Gaussian weight, mean and standard deviation (variance) to reflect the new obtained pixel value. In relation to non-matched components the weights w decreases whereas the mean and standard deviation stay the same. It is dependent upon the learning component p in relation to how fast they change. Thirdly, here we identify which components are parts of the background model. To do this a threshold value is applied to the component weights w. Fourthly, in the final step we determine the foreground pixels. Here the pixels that are identified as foreground dont match with any components determined to be the background. 6.0 Experimental Results For better results, median filter was used where the main purpose was to filter out unconnected pixels from the large connected pixels (e.g. people, vehicles) so that it was easy to distinguish between the background and foreground. With median filter the value of the output pixel is determined by the median of the neighboring pixels instead of the mean. Median has a much smaller insensitivity compared with mean to extreme values. The function used was medfilt2(A,[m n]). Figure 3 GMM Background Subtraction with high light intensity In the above image, the picture on the right shows the output of background subtraction using the GMM model. There is still false negative foreground detection which produces the disturbances in the image. Although it still shows the objects moving hence, making it suitable for object tracking. The reason for these disturbances can be narrowed down to illumination changes. Figure 4 GMM Background Subtraction with Low light intensity For the above, a short 30 second video was recorded where light intensity was moderate and the camera was still. It can be observed that the hand is visible and false negative detection is low which suggests that the model works robustly in low light intensities. From the above results, we can say that there are still improvements to be made in the algorithm mostly to work with illumination which increases the false negative background subtraction. 7.0 Further Improvements The GMM model used in this paper could be further tweaked to provide better results in terms of zero disturbances/noises. The use of morphological filters could be implemented into the algorithm to reduce these unconnected pixels which were seen in the figures above and make it more robust in object tracking. The algorithm has the capabilities of further improvements to run large size videos and importantly the ability to process more frames per seconds using larger number of Gaussians in the mixture model. Apart from the above improvements, we further plan to research the model using PTZ (Pan Tilt Zoom) technique to study the processing rate of frame/second and observe the robustness of the algorithm in terms of disturbances/noise. There are possibilities of false positive results where background could be recognised as foreground. 8.0 Conclusions In this project we implemented a very strong and widely used background subtraction method according to the paper Adaptive background mixture models for real-time tracking. This method is very adaptable for lighting changes and shadow removals. Also it finds the repetitive actions as well with the use of mixture of Gaussians. Alpha and T are the key parameters in this paper. The values of these parameters changes with the different cameras and different environment. So it is very important to get the best values for them to work with for different videos. Also we tried to improve the output quality by using some filtration methods like median filter. The guassain model is not perfect with the result since there are some disturbances after the modelling also. But there are many good post processing techniques are introduced and we referenced two of them. A new region Gaussian background model for video surveillance by Xun Cai and Long Jiang, and Improved Post-Processing for GMM based Adaptive Background Modeling by Deniz Turdu,Hakan Erdogan. But the 1st paper doesnt give any clear information about the techniques failed to explain the equations as well. Second paper introduces a very good technique and we couldnt able to get the result properly. But this assignment was very challenging for us and helped us to get a very good knowledge about different background subtraction methods used in Video surveillance. 9.0 References [1] Robust techniques for background subtraction in urban traffic video Sen-Ching S. Cheung and Chandrika Kamath, 2006/07 [2] Background Subtractions of Moving Objects https://computation.llnl.gov/casc/sapphire/background/background.html [3] Page444, Computer vision A modern approach, David A. Forsyth [4] Dr J. Zhang, CSC7006 Lecture 2 Slides, Queens University Belfast. [5] Adaptive background mixture models for real-time tracking, Chris Stafer, W.E.L Grimson [6] Improved Post processing for GMM based adaptive background modelling by Deniz Turdu, Hakan Erdogan

Heart Of Darkness Response Assignment :: essays research papers

â€Å"They were dying slowly-it was clear. They were not enemies, they were not criminals, they were nothing earthly now, nothing but black shadows of disease and starvation, lying confused in the greenish gloom†. (page 14 para. 3, line 1). The quote is coming from Marlow, upon arriving at the outer station, and first witnessing the devastation the Belgians have caused the native peoples. He is speaking about the black men, who have been enslaved, dying all around him. He can see the work they are being made to do, and finds it a great horror, similar, perhaps, to what hell must be like. This quote also shows Marlow’s first recognition to an epiphany, he will later realize, as imperialism. He says clearly, these men can not be viewed as criminals, for the only function they seemed to be carrying out was dying, and die they did, in great numbers, and at the hands of the â€Å"enlightened† Europeans. I believe his conscience was getting the better of him, first seeing the death, disease, starvation, and chaos all around, allusions of a modern day genocide, which righteous people can not stand to watch, but are helpless to do anything about it. Descriptions of Africans dying, or more precisely, being killed, are common stories surrounding imperialism. Heart of Darkness, finely details the worst kind of African imperialism, the Belgian kind. Millions of people, in what today is called the Congo, were forcefully enslaved, and then made to gather ivory tusks, and rubber plants, all the time being treated as animals, for the sole purpose of lining the pockets of the Belgian monarchy. These scenes shock the more caring, and kind hearted reader, in today’s world, and leave questions swirling in the mind about how atrocities, similar to the ones described in Heart of Darkness, could have been carried out, by a supposed more enlightened society. Surprisingly enough, European imperialists do not hold the sole rights to death and destruction. In fact, simply by reading a history book of the last 2000 years, the reader may come to the conclusion that imperialism was a natural part of empire expansion. Just look at the Egyptians , the Assyrians, the Greeks, the Romans, the Huns, the Moslems, the Christians, and finally the British. What did they all have in common, first they all conquered territory, and usually to do this they needed to kill indigenous people, so that they could use newly conquered land, for their needs.

Emerging Markets; Risks and Challenges

Trade among these countries has also grown by a staggering amount in recent years and their multinational companies are now competing with those from the developed economies. There remain, however, significant risks and challenges to investing in these countries. They are discussed here under these broad headings; Political, Economic, Legal and Socio cultural. They affect the different countries in deferent ways and sometimes Interact in deferent ways to produce deferent results. For example, political processes more often than not drive economic, legal and social policies of governments.China and India, two of the largest emerging markets operate very different political processes and therefore have two very different sets of political institutions. Chinese communism and Indian democracy vary significantly, and their political systems ultimately affect the choice of economic, legal and social policies. The first step to emerging market status for most of these countries can be trace d to political reforms and/or movements, examples being the transition from authoritarian to democratic governments and economic liberation's.It can also be argued that social reforms and/or popular movements brought about the downfall of the authoritarian governments in the first place, allowing for reforms in the political and economic systems In place (the political economy), thus paving the way for economic gains witnessed today. Despite the often complex interactions between these factors, wave attempted to simplify them by grouping them In broad categories. Emerging markets also face challenges as they come to grips with economic prosperity and their new status in the global community.

Advertising and Behavior Control Essay

In the article â€Å"Advertising and Behavior Control† there were many arguments for and against advertising. The first and the biggest problem Robert L. Arrington has against advertising is â€Å"puffery.† The reason/reasons why Arrington has a problem with puffery is due to the fact that the seller makes exaggerated, or suggestive claims about a product. His overall argument on puffery is that it isn’t just â€Å"bragging† but it is bragging that is designed to persuade you to want the item or product being sold. The bragging goes so far as they convince the viewer (of the advertisement) they need the product. Puffery ultimately leads to manipulation, exploitation, and control over what people think of the product. Although Arrington has reasons against puffery, he also points out examples as to why puffery is good for advertising. As stated on page 284 â€Å"businesses just give the consumer what he/she wants; if they didn’t they wouldn’t stay in business very long. Proof that consumer wants the precuts advertised is given by the fact that he bus them, and indeed often returns to by them again and again.† Puffery ultimately leads to the customer purchasing the product. If puffery is not used then as Arrington states businesses would go out of business very quickly. Puffery is not the only argument for or against advertising Arrington had but definitely was is strongest one. Another argument against advertising that Arrington had was credibility of an advertisement. He uses Philip Nelson’s ideas and theories to better explain his thoughts. His argument states that even when the message is not credible, we as a society give it validity by allowing ourselves to see the advertisement, and the fact that we constantly see them makes us believe that things are true. Simply put as stated on page 285 â€Å"advertised frequently, is valuable indirect information for the consumer. The reason for this is that the brands advertised most are more likely to be better buys-â€Å" Although the constant streaming of false or indirect information from advertisements makes us believe something that may be untrue, there is a counter argument as to why this form of advertising is legitimate, One of the counter arguments is in the form of a thought provoking question on page 285 â€Å"do the advertising techniques we have discussed involve a violation of human autonomy and a manipulation and control of consumer behavior, or do they simply provide an efficient and cost effective means of giving the consumer information on the basis of which he or she makes a free choice.† This may seem like a lot to grasp, but in essence the meaning of that thought provoking question is: is advertising information, or is advertising there for us to create wants and desi res in our life? These are just a couple of the arguments for and against advertising, and Arrington continues with many more thought provoking and legitimate arguments. All in all, Arrington makes claims for and against advertising, and eventually comes to the conclusion that he does not know what is right. If advertising is indeed good or bad as he says on page 289 â€Å"I do not pretend to have the answer. I only hope that the above discussion, in showing some of the kinds of harm that can be done by advertising and by indicating the likely limits of this harm, will put us in a better position to grapple with the question.† In my own opinion I don’t think that advertising should be controlled anymore than it is now. My reason for thinking this way is because products are made to be sold and along with that products are also made for us (the customer) to use, and enjoy. If products weren’t marketed/advertised to the customer as enjoyable, or brings you a higher satisfaction in life what would be the point of any person buying the product? Sure puffery may seem like it is a scam by â€Å"extending the truth† but at the same time if you had the money, and the time, and the friends to live a life like a commercial then it is very well possible to achieve that fulfillment from a product. Unfortunately not everyone is capable of doing this, but a product that is advertised is always going to be represented better than it actually is, due to the fact it is there to make a profit, and to be sold. My next point is the fact that advertisement falls into our liberties. We all have the right to freedom of speech. By advertising, or marketing one is free to say, promote, speak, etc. how they would like as long it is not in an offensive or demeaning way. This is why I believe there should be no more restrictions or limitations (if any) on advertising/marketing.

Martin Luther King I Have a Dream Essay

2. In Martin Luther King Jr’s speech he uses a variety of vivid phrases that paint a picture for the audience. For example: â€Å"crippled by the manacles of segregation and the chains of discrimination† † the negro is still languishing in the corners of society and finds himself an exile in his own land† â€Å"let us not seek to satisfy our thirst for freedom by drinking from the cup of bitterness and hatred.† These phrases attribute to gaining his audience’s attention and emotion. 3. Primary points in his speech: African American individuals are being discriminated against in society, even though the emancipation proclamation granted all slaves citizenship. America will not be tranquil as long as there is segregation. MLK and his followers will only be a part of a peaceful protest. If America is to be a great nation freedom is imperative among all people. 4. It is clear that MLK is very passionate about what he says in his speech. While staying passionate, he understands that he will not win the war against segregation by being bitter and angry. My interpretation is that he is in a way confident that this issue will be resolved, and he knows that this ongoing issue will not be resolved immediately. 5. The audience in this case would be pretty much everybody in the United States at the time as well as us today. MLK probably didn’t know that his speech would make such a big impact in our world today, but his speech was aimed toward American society as a whole. 6. Without hesitation I will say that MLK does succeed with his message. Our country today has minimal racial injustice and people can be judged by their character. I think that the reason he has succeeded is attributed to his attitude and poise that he demonstrates during the speech. His message  would not have been accepted if he was to lash out in anger and criticize the white man.

The assessment for this module is an End Course Assessment (ECA) Essay

The assessment for this module is an End Course Assessment (ECA) constituting report based on the MINI case study - Essay Example This failure can have a profound negative impact on the brand equity of the parent or corporate brand. The second positive application of this strategy deals with the addition of a new brand in the brand portfolio of the parent company. This step gives a fresh outlook to the corporate brand portfolio and enables it to nurture a â€Å"Star†. This strategy, also gives some breathing space to the aging brands of the corporation, thereby extending the lifecycle of the entire brand portfolio. Let’s analyze the marketing strategy of MINI on pertinent dimensions: 4P’s Framework: The marketing mix or 4P’s, as they are called, reflects the important strategies of the organization regarding the product, price, promotion, and place (Fill, 2009). Product The product under consideration is the MINI car, which launched its new model in 2001. In addition to this model the company has the following brands of cars: New MINI, MINI One, MINI Cooper, Cooper S, Cooper D, MINI Convertible, MINI Clubman and MINI Countryman. Price The pricing method used by the company to appraise its cars is â€Å"value-based pricing†. This method of pricing is consistent with the strong brand equity of the car. Since the car is a symbol of a specific kind of lifestyle, therefore it carries immense value for the followers of this kind of lifestyle. These followers are willing to give extra when it comes to expressing their peculiar lifestyle. This makes MINI exclusive and inaccessible to every car buyer. Promotion Industry followers have always found MINI’s marketing campaigns to be very innovative, lively and peculiar. These attributes make these campaigns very successful. When MINI launched its new 2001 model, it employed adventure campaigns to enhance brand awareness. This campaign had at its core the concept of buzz marketing. By the use of this concept the company amplified its core association of: Excitement, Uniqueness and Fun. Through this campaign th e company very successfully raised the level of awareness of its brand, and created a word of mouth effect among its customer community. This campaign employed both traditional and contemporary marketing tools and tactics, for instance media houses were brought into this campaign, print and online advertising was done along with promotional material being planted in hot spots(important public centers). Through â€Å"Bondage Fetish leather†, the company extended its adventurous and likely nature. In this entire promotional campaign the company engaged its target audience and fan community. The company employed methods which led to their (audience and community) participation. In this endeavor the company also made use of digital community forums like social networking sites e.g. Face Book, Twitter, YouTube. One unique method used in this endeavor was MINI Space, which brought together the MINI community. This community used to engage in online conversations in the form of thre ads, to communicate their opinions about the car. Direct marketing tools like emails and instant messages were also used. A unique king of email was sent to customers, containing a game in it. This mail was very

Literature. A Mexican American girl Essay Example | Topics and Well Written Essays - 500 words

Literature. A Mexican American girl - Essay Example He was an example of an unreliable narrator, due to the many facets of his character. Montresor seemed to be lacking in friendships, sensitive, but devious and vindictive and most probably slightly paranoid. He perceived himself to be above or at any rate, alienated from the 'common herd', and believed he was a connoisseur of good wines. This last, he used as a trick to achieve vengeance on a supposed enemy. He appeared to be a complete loner through choice, and looked down on the rich who had not the old family heritage he possessed. He could be described equally as a sad, pitiful individual or a dangerous mad man. Esperanza would put a Want Ad. in the paper for a used portable typewriter. She knew she had talent; her aunt Lupe told her to keep writing. She valued education, working to pay for it, and admired Alicia for studying. A typewriter would be a good start for her dreams to be made real. He would put an obsequious obituary in the paper, as this would give him an opportunity to gloat over his success at achieving redress, while removing suspicion from himself. It would also allow him to appear to be mourning publicly and reinforce the faade of his and Fortunato's alleged friendship. He would use this as a way to state his superiority over others in his society. Her tone and style would be serious