Sunday, January 26, 2020

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 [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

Saturday, January 18, 2020

Prosecuting Cyber Bullying

Technology is frequently being used to display personal Information on social networking sites for everyone In the world to see. With this Instantaneous technology, the school bully has access to an easier, more harmful, and anonymous way to intimidate their victim. Cyber bullying has become a form of harassment that is creating a myriad of problems for teenagers and, therefore, needs to be dealt with properly.The sense of anonymity and the ability to disguise ones identity online increases Weber bullying activity by â€Å"making fun of, telling lies, spreading rumors, threats and sharing private Information or pictures [online]† (Lulls lines 31-32). In spite of the recent technological advancements, problems with bullying has existed for generations. At the click of a button, the threatening information that can be posted online can be seen throughout the world for anyone to see.Because more people have access to someone's private or embarrassing information, â€Å"an increa sed audience can often lead to more harmful bullying Incidents† (Macaque 27). Cyber lulling can be committed any where and at any time, therefore â€Å"the cyber bullies may not fully understand the Impact of their behavior on their victims† (Lulls line 9). Cyber bullying can negatively affect an individual and even lead to depression and thoughts of suicide. There are many well known stories of teenagers compelled to desperate, even suicidal acts after having been exposed to recurrent harassment by others online.For Instance, the devastating and well-known case off 13-year-old girl named Megan Meier, committed seclude allegedly due to cyber bullying (Macaque 141 After coming friends with a boy she met online, Megan, who had a lifelong struggle with weight and self-esteem, finally met a boy she thought she could trust. After weeks of friendly conversations online Megan was eager to strengthen their relationship by finally meeting each other in person. Flirtatious messag es from her online boyfriend â€Å"Josh† suddenly turned into disturbing and confusing remarks such as â€Å"the world would be a better place without you† (Macaque 16). Josh† even began to post spiteful comments on his site about Megan. Megan committed seclude to escape the main and humiliation she was put through (â€Å"Social Networking Web Sites† 3). Investigators soon discovered that the profile of â€Å"Josh Evans† was created by a 48- year-old woman named Lori Drew. Loris's intentions were to tease and embarrass Megan, her next door neighbor and a former friend of Loris's 13-year-old daughter (Macaque 28). This case brought national attention to the dire consequences of cyber bullying. The community was outraged and demanded charges to be brought against this mother.Lori went to court but was never convicted because there was no law against cyber bullying. Another instance of the horrific effects of cyber bullying is portrayed in the tragic s tory of 18-year-old Tyler Clementine. Tyler started his freshman year with a big future ahead of him and a great outlook on life. Invading Teller's privacy, his college roommate streamed private footage online from a WebMD that he held In their dorm (â€Å"Key Events in the History of Prosecuting Cybernetics† 8). Comments about his sexuality flooded through his inbox that night.Days after the video went viral, Tyler could not handle the embarrassment and committed suicide by â€Å"Jumping off of the George Washington Bridge† (â€Å"Prosecuting Cybernetics† par. 1). These are two highly publicized and tragic cases resulting from abuse online. There are thousands of other people that are being threatened and bullied through technology every day and no one knows about it. There is nothing positive that results from bullying, but some people still believe that cyber bullies should not be prosecuted because it is not a crime.Critics say that cyber bullying is not an issue that should have legal attention, â€Å"but a dilemma best solved by schools and parents† (â€Å"Prosecuting Cybernetics† par. 2). Many school districts throughout the country are attempting to educate their students about online safety and protection. In order to solve the problem of cyber bullying, the critics believe that schools should concentrate on â€Å"spreading messages of tolerance and civility to all young people† (â€Å"Prosecuting Cybernetics† par. 2). Another point that critics stress is that prosecuting cyber bullies violates â€Å"the First Amendment's guarantee of free speech† (Hayward 21).Opponents to the prosecution of cyber bullies need to take into account the innocent people that are being terrorized online ND their rights to protect themselves. Supporters of the prosecution of these online abusers agree that cyber bullying leads to many emotional problems for the victims. Cyber bullies purposely â€Å"target their vict ims, revealing personal information on the Internet or harassing them through frequent or threatening messages† (â€Å"Prosecuting Cybernetics† par. 16). More often than not, school administrators and parents fail to identify and stop cyber bullying before it is too late.Prosecutors need to take action in order to protect students online. Students are apprehensive to tell an adult about abuse online because they fear the bullying will only get worse. Victims feel that they are on their own in the endless abuse and â€Å"allegedly saw no choice for escape except to kill themselves† (Lulls 22). Historically, a person repeatedly bullied is not only a danger to themselves, but may even have the urge to harm others. For example, in the article â€Å"Prosecuting Cybernetics†, research has determined that â€Å"perpetrators of school violence?such as the two students who went on a shooting rampage at ColumbineHigh School in Colorado in 1999?often have a history of both bullying and being bullied themselves† (par. 31). Even though the cyber bully is not physically shoving the victim around, they are still able to instill a sense of fear and hopelessness in their victims. In order to punish cyber bullies for their conduct, new, up-to-date laws need to be passed for the protection of the innocent victim. With technology being the most popular form of communication, it is important to establish a law to â€Å"deter future bullying with a legal means to punish those who cause harm†.

Friday, January 10, 2020

The Research Paper Writing Help Diaries

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Wednesday, January 1, 2020

Getting Fit Dialogue and Reading

Getting fit in English refers to doing exercise in order to feel better and live a more healthy lifestyle. People often go to the gym to get in shape or get fit. While theyre at the gym theyll do a wide variety of exercises such as push-ups and sit-ups. Its important to always do stretching exercises as well, these should be done both before and after you go to the gym.   At they gym, youll fid lots of equipment such as weight lifting machines, exercise bikes, ellipticals, and treadmills. Most health clubs also offer jogging tracks and areas for aerobics, as well as classes in fitness activities such as Zumba, or spinning classes. Most gyms offer changing rooms nowadays. Some even have whirlpools, steam rooms, and saunas to help you relax and unwind your muscles after a long hard workout. The important thing to remember when getting fit is that you need to be consistent. In other words, youll need to go to the gym on a regular basis. Maybe three or four times a week. Its a good idea to do a wide range of exercises rather than focus on just one such as weight lifting. For example, do fifteen minutes of stretching and aerobics, combined with a half hour of bike riding and another fifteen minutes of weight lifting on two days of the week. On the other two, play some basketball, go jogging and use the elliptical. Varying your routine will help keep you coming back, as well as help keep your whole body fit.   In the Gym Dialogue Hello, my name is Jane and Id like to ask a few questions about getting fit.Hi, Jane. What can I do for you? I need to get in shape.Well, youve come to the right place. Have you been doing any exercise lately? Im afraid not.OK. Well start off slow. Which type of exercise do you enjoy doing? I like doing aerobics, but I hate jogging. I dont mind doing some weight-lifting, though.Great, that gives us plenty to work with. How often can you work out? Twice or three times a week would be good.Why dont we start with an aerobics class twice a week followed by a little weight lifting? Sounds fine to me.Youll need to start slowly and build up gradually to three or four times a week. OK. What kind of equipment will I need?Youll need a leotard and some sneakers. Is that all? How do I sign up for the classes?Well need you to join the gym and then you can choose which classes fit your schedule best. Great! I cant wait to get started. Thanks for your advice.No problem. Ill see you in aerobics class! Key Vocabulary from Reading and Dialogue (do) exerciseadviceaerobicschanging roomellipticalequipmentexercise bikeget fit  get in shape  joggingjoinleotardpush up  saunasign upsit-upsneakersspinning classsteam roomstretchingtreadmillunwindweight lifting machinesweight liftingwhirlpool  Zumba More Intermediate Level Dialogues Making SuggestionsOn the TelephoneSeeing a DoctorDescribing a Friend - Uses of Like