├── Frame1.png ├── Frame2.png ├── Frame3.png ├── Frame4.png ├── Frame5.png ├── Frame6.png ├── LMatch2.mp4 ├── Presentation.pptx ├── README.md ├── Running ├── @walking73.mp4 ├── @walking75.mp4 ├── @walking77.mp4 ├── @walking78.mp4 ├── @walking80.mp4 ├── @walking81.mp4 ├── @walking82.mp4 ├── @walking83.mp4 ├── AskSir.mp4 ├── walking.mp4 ├── walking10.mp4 ├── walking100.mp4 ├── walking101.mp4 ├── walking102.mp4 ├── walking103.mp4 ├── walking104.mp4 ├── walking105.mp4 ├── walking106.mp4 ├── walking107.mp4 ├── walking109.mp4 ├── walking110.mp4 ├── walking12.mp4 ├── walking13.mp4 ├── walking14.mp4 ├── walking15.mp4 ├── walking17.mp4 ├── walking18.mp4 ├── walking2.mp4 ├── walking27.mp4 ├── walking30.mp4 ├── walking35.mp4 ├── walking37.mp4 ├── walking4.mp4 ├── walking40.mp4 ├── walking46.mp4 ├── walking47.mp4 ├── walking48.mp4 ├── walking49.mp4 ├── walking5.mp4 ├── walking50.mp4 ├── walking51.mp4 ├── walking52.mp4 ├── walking53.mp4 ├── walking54.mp4 ├── walking55.mp4 ├── walking56.mp4 ├── walking57.mp4 ├── walking58.mp4 ├── walking59.mp4 ├── walking60.mp4 ├── walking61.mp4 ├── walking62.mp4 ├── walking63.mp4 ├── walking64.mp4 ├── walking65.mp4 ├── walking66.mp4 ├── walking67.mp4 ├── walking68.mp4 ├── walking69.mp4 ├── walking7.mp4 ├── walking70.mp4 ├── walking71.mp4 ├── walking72.mp4 ├── walking73.mp4 ├── walking74.mp4 ├── walking75.mp4 ├── walking76.mp4 ├── walking77.mp4 ├── walking78.mp4 ├── walking79.mp4 ├── walking8.mp4 ├── walking80.mp4 ├── walking81.mp4 ├── walking82.mp4 ├── walking83.mp4 ├── walking84.mp4 ├── walking85.mp4 ├── walking86.mp4 ├── walking87.mp4 ├── walking88.mp4 ├── walking89.mp4 ├── walking9.mp4 ├── walking90.mp4 ├── walking91.mp4 ├── walking92.mp4 ├── walking93.mp4 ├── walking94.mp4 ├── walking95.mp4 ├── walking96.mp4 ├── walking97.mp4 ├── walking98.mp4 └── walking99.mp4 ├── Test1 ├── Dataset │ ├── Dribble │ │ ├── Dribble (1).mp4 │ │ └── Dribble (2).mp4 │ ├── Kick │ │ ├── Kick (1).mp4 │ │ ├── Kick (10).mp4 │ │ ├── Kick (11).mp4 │ │ ├── Kick (12).mp4 │ │ ├── Kick (13).mp4 │ │ ├── Kick (14).mp4 │ │ ├── Kick (15).mp4 │ │ ├── Kick (2).mp4 │ │ ├── Kick (3).mp4 │ │ ├── Kick (4).mp4 │ │ ├── Kick (5).mp4 │ │ ├── Kick (6).mp4 │ │ ├── Kick (7).mp4 │ │ ├── Kick (8).mp4 │ │ └── Kick (9).mp4 │ ├── Run │ │ ├── Run (1).mp4 │ │ ├── Run (10).mp4 │ │ ├── Run (11).mp4 │ │ ├── Run (12).mp4 │ │ ├── Run (13).mp4 │ │ ├── Run (14).mp4 │ │ ├── Run (15).mp4 │ │ ├── Run (16).mp4 │ │ ├── Run (17).mp4 │ │ ├── Run (2).mp4 │ │ ├── Run (3).mp4 │ │ ├── Run (4).mp4 │ │ ├── Run (5).mp4 │ │ ├── Run (6).mp4 │ │ ├── Run (7).mp4 │ │ ├── Run (8).mp4 │ │ └── Run (9).mp4 │ └── Walk │ │ ├── Walk (1).mp4 │ │ ├── Walk (10).mp4 │ │ ├── Walk (11).mp4 │ │ ├── Walk (12).mp4 │ │ ├── Walk (13).mp4 │ │ ├── Walk (14).mp4 │ │ ├── Walk (15).mp4 │ │ ├── Walk (16).mp4 │ │ ├── Walk (2).mp4 │ │ ├── Walk (3).mp4 │ │ ├── Walk (4).mp4 │ │ ├── Walk (5).mp4 │ │ ├── Walk (6).mp4 │ │ ├── Walk (7).mp4 │ │ ├── Walk (8).mp4 │ │ └── Walk (9).mp4 ├── Demo.m ├── ExtractGHOF.m ├── ExtractGLBP.m ├── ExtractLHOF.m ├── ExtractLLBP.m ├── ExtractSoccerVDOData.m ├── GlobalFeaConcat.m ├── LocalFeaConcat.m ├── Modified_LBP.m ├── SequentialData.mat ├── ShuffleData.m ├── calFuzzyHist.m └── hist_fine2coarseMapping.m ├── Test2 ├── Demo.m ├── ExtractGLBP.m ├── ExtractLLBP.m ├── Modified_LBP.m └── Run (1).mp4 ├── Test4 ├── Kick.mp4 ├── KickTest.mp4 ├── Run.mp4 ├── RunTest.mp4 └── Test1.m ├── VDO_Frame-min.png ├── VDO_Frame.png ├── dribling ├── drib2.mp4 ├── drib4.mp4 ├── dribling1.mp4 ├── dribling10_NOT.mp4 ├── dribling11.mp4 ├── dribling12.mp4 ├── dribling13.mp4 ├── dribling14.mp4 ├── dribling15.mp4 ├── dribling16.mp4 ├── dribling17.mp4 ├── dribling18.mp4 ├── dribling19.mp4 ├── dribling20.mp4 ├── dribling21.mp4 ├── dribling22.mp4 ├── dribling23.mp4 ├── dribling24.mp4 ├── dribling25.mp4 ├── dribling27.mp4 ├── dribling28.mp4 ├── dribling29.mp4 ├── dribling3.mp4 ├── dribling30.mp4 ├── dribling31.mp4 ├── dribling32.mp4 ├── dribling34.mp4 ├── dribling35.mp4 ├── dribling37.mp4 ├── dribling38.mp4 ├── dribling39.mp4 ├── dribling4.mp4 ├── dribling40.mp4 ├── dribling41.mp4 ├── dribling42.mp4 ├── dribling43.mp4 ├── dribling44.mp4 ├── dribling45.mp4 ├── dribling46.mp4 ├── dribling47.mp4 ├── dribling48.mp4 ├── dribling49.mp4 ├── dribling4_NOT.mp4 ├── dribling50.mp4 ├── dribling51.mp4 ├── dribling52.mp4 ├── dribling53.mp4 ├── dribling54.mp4 ├── dribling55.mp4 ├── dribling56.mp4 ├── dribling57.mp4 ├── dribling58.mp4 ├── dribling59.mp4 ├── dribling6.mp4 ├── dribling60.mp4 ├── dribling61.mp4 ├── dribling62.mp4 ├── dribling64.mp4 ├── dribling65.mp4 ├── dribling66.mp4 ├── dribling67.mp4 ├── dribling68.mp4 ├── dribling69.mp4 ├── dribling7.mp4 ├── dribling70.mp4 ├── dribling71.mp4 ├── dribling72.mp4 ├── dribling73.mp4 ├── dribling74.mp4 ├── dribling75.mp4 ├── dribling76.mp4 ├── dribling77.mp4 ├── dribling78.mp4 ├── dribling79.mp4 ├── dribling8.mp4 ├── dribling80.mp4 ├── dribling81.mp4 ├── dribling82.mp4 ├── dribling83.mp4 ├── dribling85.mp4 ├── dribling86.mp4 ├── dribling87.mp4 ├── dribling88.mp4 ├── dribling89.mp4 ├── dribling9.mp4 ├── dribling90.mp4 └── dribling91.mp4 ├── ezgif.com-crop10.mp4 ├── ezgif.com-crop11.mp4 ├── ezgif.com-crop12.mp4 ├── ezgif.com-crop13.mp4 ├── ezgif.com-crop14.mp4 ├── ezgif.com-crop15.mp4 ├── ezgif.com-crop16.mp4 ├── ezgif.com-crop17.mp4 ├── ezgif.com-crop18.mp4 ├── ezgif.com-crop19.mp4 ├── ezgif.com-crop20.mp4 ├── ezgif.com-crop21.mp4 ├── ezgif.com-crop22.mp4 ├── ezgif.com-crop23.mp4 ├── ezgif.com-crop24.mp4 ├── ezgif.com-crop25.mp4 ├── ezgif.com-crop26.mp4 ├── ezgif.com-crop27.mp4 ├── ezgif.com-crop28.mp4 ├── ezgif.com-crop29.mp4 ├── ezgif.com-crop30.mp4 ├── ezgif.com-crop31.mp4 ├── ezgif.com-crop32.mp4 ├── ezgif.com-crop33.mp4 ├── ezgif.com-crop34.mp4 ├── ezgif.com-crop35.mp4 ├── ezgif.com-crop36.mp4 ├── ezgif.com-crop37.mp4 ├── ezgif.com-crop38.mp4 ├── ezgif.com-crop39.mp4 ├── ezgif.com-crop4.mp4 ├── ezgif.com-crop40.mp4 ├── ezgif.com-crop41.mp4 ├── ezgif.com-crop42.mp4 ├── ezgif.com-crop43.mp4 ├── ezgif.com-crop44.mp4 ├── ezgif.com-crop45.mp4 ├── ezgif.com-crop46.mp4 ├── ezgif.com-crop47.mp4 ├── ezgif.com-crop48.mp4 ├── ezgif.com-crop49.mp4 ├── ezgif.com-crop5.mp4 ├── headsort ├── 002.mp4 ├── 003.mp4 ├── 004.mp4 ├── 006.mp4 ├── 007.mp4 ├── 008.mp4 ├── 009.mp4 ├── 010.mp4 ├── 011.mp4 ├── 012.mp4 ├── 013.mp4 ├── 014.mp4 ├── 15.mp4 ├── 16.mp4 ├── 173.mp4 ├── 18.mp4 ├── 19.mp4 ├── 20.mp4 └── 21.mp4 ├── kicking ├── NEWKICK!.mp4 ├── NEWKICK1.mp4 ├── NEWKICK10.mp4 ├── NEWKICK11.mp4 ├── NEWKICK12.mp4 ├── NEWKICK13.mp4 ├── NEWKICK14.mp4 ├── NEWKICK15.mp4 ├── NEWKICK16.mp4 ├── NEWKICK17.mp4 ├── NEWKICK18.mp4 ├── NEWKICK19.mp4 ├── NEWKICK2!.mp4 ├── NEWKICK20.mp4 ├── NEWKICK21.mp4 ├── NEWKICK22.mp4 ├── NEWKICK23.mp4 ├── NEWKICK24.mp4 ├── NEWKICK25.mp4 ├── NEWKICK26.mp4 ├── NEWKICK27.mp4 ├── NEWKICK28.mp4 ├── NEWKICK29.mp4 ├── NEWKICK3.mp4 ├── NEWKICK30.mp4 ├── NEWKICK31.mp4 ├── NEWKICK32.mp4 ├── NEWKICK33.mp4 ├── NEWKICK34.mp4 ├── NEWKICK35.mp4 ├── NEWKICK356mp4 ├── NEWKICK36.mp4 ├── NEWKICK37.mp4 ├── NEWKICK38.mp4 ├── NEWKICK39.mp4 ├── NEWKICK4.mp4 ├── NEWKICK5.mp4 ├── NEWKICK6.mp4 ├── NEWKICK7.mp4 ├── NEWKICK8.mp4 ├── NEWKICK9.mp4 └── Untitled Folder │ ├── kick1.mp4 │ ├── kick2.mp4 │ ├── kick4.mp4 │ └── kick5.mp4 └── running ├── running1.mp4 ├── running10.mp4 ├── running11.mp4 ├── running12.mp4 ├── running13.mp4 ├── running2.mp4 ├── running3.mp4 ├── running4.mp4 ├── running5.mp4 └── running6.mp4 /Frame1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mavenzer/Player-Action-Detection/88952dc5b97a4487376403d191da7835a458dd7e/Frame1.png -------------------------------------------------------------------------------- /Frame2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mavenzer/Player-Action-Detection/88952dc5b97a4487376403d191da7835a458dd7e/Frame2.png 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Player-Action-Detection 2 | ## Abstract 3 | Human action recognition is an important research area in the field of computer vision having a great number of real-world applications.This project presents soccer player action recognition framework that extracts player silhouette clues, analyzes scene dynamics and interprets player behaviors. Different features have been considered forthe player action recognition, some of them concerning the player silhouette analysis, and some others related to the player kinematics.Experiments were carried out on a multi view image sequences of a self created soccer action dataset. 4 | 5 | ## Introduction 6 | Human action recognition is considered an important problem in the field of computer vision and the ever growing interest in it is fueled, in the great number of the real-world applications such as surveillance scenarios, content-based image retrieval, human-robot interaction,sport video analysis etc. This project work can be widely used in the different domains and categories. Action primitives are atomic movements that considers action primitive, action and activity. Activity contains a numbers of actions and provide an interpretation of movements that is beings performed. A video sequence is represented as the a collection of spatial-temporal words by extracting space time interest points. In order to capture the imaging variation and attributes.of individuals, two types of features are fused for the local spatial-temporal volume. 7 | 8 | The majority of existing frameworks for action recognition consist of three main steps: feature extraction, dictionary learning to form a representation for a video based on the extracted features, and finally classification of th video using the representation. These features are supposed to encode the information which is useful for recognition of the action in a numerical form such as a vector. Then, the extracted features are used for forming a representation of a video, which captures the actions that occur therein. Such representation may be as simple as a histogram of most frequent motions or a semantically meaningful model such as action poses. Automated methods for the sports video understanding can help us in the localization of the salientactions of a game. Many recent methods exits to solve generic human activity localization in the video focusing on sport. However, detecting soccer actions is a difficult task due to sparsity of the events in the video. 9 | 10 | ## Here are the some screenshort given for the demostration purpose 11 | ![](Frame1.png) 12 | ![](Frame2.png) 13 | ![](Frame3.png) 14 | ![](Frame4.png) 15 | ![](Frame5.png) 16 | ![](Frame6.png) 17 | 18 | # I have combined some of the images 19 | ![](VDO_Frame-min.png) 20 | ![](VDO_Frame.png) 21 | -------------------------------------------------------------------------------- /Running/@walking73.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mavenzer/Player-Action-Detection/88952dc5b97a4487376403d191da7835a458dd7e/Running/@walking73.mp4 -------------------------------------------------------------------------------- /Running/@walking75.mp4: -------------------------------------------------------------------------------- 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7 | Pattern = 'Results\'; 8 | 9 | % For Added Feature 10 | Par = 'AddFea'; 11 | Pattern = [Pattern 'AD_']; 12 | 13 | % For concatinated Feature 14 | % Par = 'Concat'; 15 | % Pattern = [Pattern 'CON_']; 16 | 17 | load('SequentialData.mat'); 18 | SDS = ShuffleData(DS); 19 | for i = 1:length(SDS.Data) 20 | 21 | % For Histogram of Optical Flow applied Globally 22 | if i == 1 23 | Pattern = [Pattern 'GHOF']; 24 | end 25 | Result.FV(i,:) = ExtractGHOF(SDS.Data(i,1),Par); 26 | 27 | % For Histogram of Optical Flow applied Locally 28 | % if i == 1 29 | % Pattern = [Pattern 'LHOF']; 30 | % end 31 | % Result.FV(i,:) = ExtractLHOF(SDS.Data(i,1),Par); 32 | 33 | % For Local Binary Pattern applied Globally 34 | % if i == 1 35 | % Pattern = [Pattern 'GLBP']; 36 | % end 37 | % Result.FV(i,:) = ExtractGLBP(SDS.Data(i,1),Par); 38 | 39 | % For Local Binary Pattern applied Locally 40 | % if i == 1 41 | % Pattern = [Pattern 'LLBP']; 42 | % end 43 | % Result.FV(i,:) = ExtractLLBP(SDS.Data(i,1),Par); 44 | 45 | % For Concatinated Globally applied Feature 46 | % if i == 1 47 | % Pattern = [Pattern 'GHOF_GLBP']; 48 | % end 49 | % Result.FV(i,:) = GlobalFeaConcat(SDS.Data(i,1),Par); 50 | 51 | % For Concatinated locally applied Feature 52 | % if i == 1 53 | % Pattern = [Pattern 'LHOF_LLBP']; 54 | % end 55 | % Result.FV(i,:) = LocalFeaConcat(SDS.Data(i,1),Par); 56 | 57 | end 58 | clear DS; 59 | NF = 10; 60 | cvFolds = crossvalind('Kfold', SDS.Labelidx, NF); 61 | Result.cp(1:NF) = classperf(SDS.Labelidx); 62 | 63 | for i = 1:NF 64 | testIdx = (cvFolds == i); 65 | trainIdx = ~testIdx; 66 | 67 | % SVM Classifier 68 | if i == 1 69 | Pattern = [Pattern '_SVM']; 70 | end 71 | Model = fitrsvm(Result.FV(trainIdx,:), SDS.Labelidx(trainIdx), 'KernelFunction','rbf'); 72 | pred = round(predict(Model,Result.FV(testIdx,:))); 73 | 74 | % Random Forest Classier 75 | % if i == 1 76 | % Pattern = [Pattern '_RF']; 77 | % end 78 | % NT = 10; 79 | % Model = TreeBagger(NT, Result.FV(trainIdx,:), SDS.Labelidx(trainIdx)); 80 | % predArr = cell2mat(predict(Model,Result.FV(testIdx,:))); 81 | % for j = 1:length(predArr) 82 | % pred(j,1) = str2double(predArr(j)); 83 | % end 84 | % clear predArr; 85 | 86 | % Measuring Individual Performance 87 | Result.cp(i) = classperf(Result.cp(i), pred, testIdx); 88 | Result.Accuracy(i) = (Result.cp(i).CorrectRate)*100; 89 | clear pred Model; 90 | end 91 | 92 | % Measuring Overall Performance 93 | Result.MinAccuracy = min(Result.Accuracy); 94 | Result.MeanAccuracy = mean(Result.Accuracy); 95 | Result.MaxAccuracy = max(Result.Accuracy); 96 | save([Pattern '_Result.mat'],'Result'); 97 | clearvars -except Result; 98 | -------------------------------------------------------------------------------- /Test1/ExtractGHOF.m: -------------------------------------------------------------------------------- 1 | function [ videoFeature ] = ExtractGHOF( V, P ) 2 | % Input: V -> VideoReader object for the video 3 | % P -> States Added or concatinated feature 4 | % Output: videoFeature -> HOF feature vector 5 | 6 | for i = 1:25 7 | videoVol(:,:,i) = rgb2gray(readFrame(V)); 8 | end 9 | opticFlow = opticalFlowFarneback; 10 | % Extract features 11 | % ------------------------------------------------------------------------- 12 | shiAll = []; % the variable that contains all the features. 13 | for v2 = 1:size(videoVol,3) % for each frame 14 | 15 | flow = estimateFlow(opticFlow,videoVol(:,:,v2)); % estimate field flow 16 | if v2 < 2 % drop the first frame in the sequence 17 | continue 18 | end 19 | 20 | coarseHist = calFuzzyHist(flow); % compute feature vector for each frame 21 | if P == 'Concat' 22 | shiAll = cat(2,shiAll,coarseHist); % concatenate the features for all frames 23 | else 24 | if v2 == 2 25 | shiAll = coarseHist; 26 | else 27 | shiAll = shiAll + coarseHist; 28 | end 29 | end 30 | end 31 | 32 | videoFeature = shiAll; % the feture that describes the motion in terms of angular histograms in the video 33 | 34 | 35 | end 36 | 37 | -------------------------------------------------------------------------------- /Test1/ExtractGLBP.m: -------------------------------------------------------------------------------- 1 | function [ videoFeature ] = ExtractGLBP( V, P ) 2 | % Input: V -> VideoReader object for the video 3 | % P -> States Added or concatinated feature 4 | % Output: videoFeature -> LBP feature vector 5 | 6 | for i = 1:25 7 | videoVol(:,:,i) = rgb2gray(readFrame(V)); 8 | end 9 | shiAll = []; % the variable that contains all the features 10 | for v2 = 1:size(videoVol,3) % for each frame 11 | LBPFeature = Modified_LBP(videoVol(:,:,v2)); % compute feature vector for each frame 12 | if P == 'Concat' 13 | shiAll = cat(2,shiAll,LBPFeature); % concatenate the features for all frames 14 | else 15 | if v2 == 1 16 | shiAll = LBPFeature; 17 | else 18 | shiAll = shiAll + LBPFeature; 19 | end 20 | end 21 | end 22 | videoFeature = shiAll; % the feture that describes the LBP in the video 23 | 24 | end 25 | 26 | -------------------------------------------------------------------------------- /Test1/ExtractLHOF.m: -------------------------------------------------------------------------------- 1 | function [ videoFeature ] = ExtractLHOF( V, P ) 2 | % Input: V -> VideoReader object for the video 3 | % P -> States Added or concatinated feature 4 | % Output: videoFeature -> HOF feature vector 5 | 6 | for i = 1:25 7 | videoVol(:,:,i) = rgb2gray(readFrame(V)); 8 | end 9 | opticFlow = opticalFlowFarneback; 10 | % Extract features 11 | % ------------------------------------------------------------------------- 12 | shiAll = []; % the variable that contains all the features 13 | for v2 = 1:size(videoVol,3) % for each frame 14 | 15 | % estimate local field flows 16 | flow1 = estimateFlow(opticFlow,videoVol(1:50,1:50,v2)); 17 | flow2 = estimateFlow(opticFlow,videoVol(1:50,51:100,v2)); 18 | flow3 = estimateFlow(opticFlow,videoVol(1:50,101:150,v2)); 19 | flow4 = estimateFlow(opticFlow,videoVol(51:100,1:50,v2)); 20 | flow5 = estimateFlow(opticFlow,videoVol(51:100,51:100,v2)); 21 | flow6 = estimateFlow(opticFlow,videoVol(51:100,101:150,v2)); 22 | flow7 = estimateFlow(opticFlow,videoVol(101:150,1:50,v2)); 23 | flow8 = estimateFlow(opticFlow,videoVol(101:150,51:100,v2)); 24 | flow9 = estimateFlow(opticFlow,videoVol(101:150,101:150,v2)); 25 | if v2 < 2 % drop the first frame in the sequence 26 | continue 27 | end 28 | 29 | % compute feature vector for each frame parts 30 | coarseHist = calFuzzyHist(flow1); 31 | coarseHist = cat(2,coarseHist,calFuzzyHist(flow2)); 32 | coarseHist = cat(2,coarseHist,calFuzzyHist(flow3)); 33 | coarseHist = cat(2,coarseHist,calFuzzyHist(flow4)); 34 | coarseHist = cat(2,coarseHist,calFuzzyHist(flow5)); 35 | coarseHist = cat(2,coarseHist,calFuzzyHist(flow6)); 36 | coarseHist = cat(2,coarseHist,calFuzzyHist(flow7)); 37 | coarseHist = cat(2,coarseHist,calFuzzyHist(flow8)); 38 | coarseHist = cat(2,coarseHist,calFuzzyHist(flow9)); 39 | if P == 'Concat' 40 | shiAll = cat(2,shiAll,coarseHist); % concatenate the features for all frames 41 | else 42 | if v2 == 2 43 | shiAll = coarseHist; 44 | else 45 | shiAll = shiAll + coarseHist; 46 | end 47 | end 48 | end 49 | 50 | videoFeature = shiAll; % the feture that describes the motion in terms of angular histograms in the video 51 | 52 | 53 | end 54 | -------------------------------------------------------------------------------- /Test1/ExtractLLBP.m: -------------------------------------------------------------------------------- 1 | function [ videoFeature ] = ExtractLLBP( V, P ) 2 | % Input: V -> VideoReader object for the video 3 | % P -> States Added or concatinated feature 4 | % Output: videoFeature -> LBP feature vector 5 | 6 | for i = 1:25 7 | videoVol(:,:,i) = rgb2gray(readFrame(V)); 8 | end 9 | shiAll = []; % the variable that contains all the features 10 | for v2 = 1:size(videoVol,3) % for each frame 11 | 12 | % compute feature vector for each frame parts 13 | LBPFeature = Modified_LBP(videoVol(1:50,1:50,v2)); 14 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(1:50,51:100,v2))); 15 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(1:50,101:150,v2))); 16 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(51:100,1:50,v2))); 17 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(51:100,51:100,v2))); 18 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(51:100,101:150,v2))); 19 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(101:150,1:50,v2))); 20 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(101:150,51:100,v2))); 21 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(101:150,101:150,v2))); 22 | if P == 'Concat' 23 | shiAll = cat(2,shiAll,LBPFeature); % concatenate the features for all frames 24 | else 25 | if v2 == 1 26 | shiAll = LBPFeature; 27 | else 28 | shiAll = shiAll + LBPFeature; 29 | end 30 | end 31 | end 32 | videoFeature = shiAll; % the feture that describes the LBP in the video 33 | 34 | end 35 | -------------------------------------------------------------------------------- /Test1/ExtractSoccerVDOData.m: -------------------------------------------------------------------------------- 1 | function ExtractSoccerVDOData( ) 2 | %Output : Saves the structure of Data (DS) 3 | % FileName -> 'SequentialData.mat' 4 | % DS -> Structure containing Dataset 5 | % DS.Data -> VideoReader object of the video data 6 | % DS.Labelidx -> Corresponding Label index 7 | % DS.Label -> Corresponding Labels 8 | 9 | ind = 1; 10 | addpath('Dataset\Dribble'); 11 | i = 1; 12 | while(1) 13 | DS.Label{1,1} = 'Dribble'; 14 | str = ['Dribble (' int2str(i) ').mp4']; 15 | if exist(str, 'file') 16 | DS.Data(ind,1) = VideoReader(str); 17 | DS.Labelidx(ind,1) = 1; 18 | ind = ind + 1; 19 | i = i + 1; 20 | else 21 | break; 22 | end 23 | end 24 | addpath('Dataset\Kick'); 25 | i = 1; 26 | while(1) 27 | DS.Label{2,1} = 'Kick'; 28 | str = ['Kick (' int2str(i) ').mp4']; 29 | if exist(str, 'file') 30 | DS.Data(ind,1) = VideoReader(str); 31 | DS.Labelidx(ind,1) = 2; 32 | ind = ind + 1; 33 | i = i + 1; 34 | else 35 | break; 36 | end 37 | end 38 | addpath('Dataset\Run'); 39 | i = 1; 40 | while(1) 41 | DS.Label{3,1} = 'Run'; 42 | str = ['Run (' int2str(i) ').mp4']; 43 | if exist(str, 'file') 44 | DS.Data(ind,1) = VideoReader(str); 45 | DS.Labelidx(ind,1) = 3; 46 | ind = ind + 1; 47 | i = i + 1; 48 | else 49 | break; 50 | end 51 | end 52 | addpath('Dataset\Walk'); 53 | i = 1; 54 | while(1) 55 | DS.Label{4,1} = 'Walk'; 56 | str = ['Walk (' int2str(i) ').mp4']; 57 | if exist(str, 'file') 58 | DS.Data(ind,1) = VideoReader(str); 59 | DS.Labelidx(ind,1) = 4; 60 | ind = ind + 1; 61 | i = i + 1; 62 | else 63 | break; 64 | end 65 | end 66 | % addpath('Dataset\Head'); 67 | % i = 1; 68 | % while(1) 69 | % DS.Label{5,1} = 'Head'; 70 | % str = ['Walk (' int2str(i) ').mp4']; 71 | % if exist(str, 'file') 72 | % DS.Data(ind,1) = VideoReader(str); 73 | % DS.Labelidx(ind,1) = 5; 74 | % ind = ind + 1; 75 | % i = i + 1; 76 | % else 77 | % break; 78 | % end 79 | % end 80 | save('SequentialData.mat','DS'); 81 | 82 | end 83 | 84 | -------------------------------------------------------------------------------- /Test1/GlobalFeaConcat.m: -------------------------------------------------------------------------------- 1 | function [ videoFeature ] = GlobalFeaConcat( V, P ) 2 | % Input: V -> VideoReader object for the video 3 | % P -> States Added or concatinated feature 4 | % Output: videoFeature -> Concatinated LHOF and LLBP feature vector 5 | 6 | shiAll = []; % the variable that contains all the features. 7 | shiAll = cat(2,shiAll,ExtractGHOF(V,P)); 8 | V.CurrentTime = 0; 9 | shiAll = cat(2,shiAll,ExtractGLBP(V,P)); 10 | videoFeature = shiAll; 11 | 12 | end 13 | 14 | -------------------------------------------------------------------------------- /Test1/LocalFeaConcat.m: -------------------------------------------------------------------------------- 1 | function [ videoFeature ] = LocalFeaConcat( V, P ) 2 | % Input: V -> VideoReader object for the video 3 | % P -> States Added or concatinated feature 4 | % Output: videoFeature -> Concatinated LHOF and LLBP feature vector 5 | 6 | shiAll = []; % the variable that contains all the features. 7 | shiAll = cat(2,shiAll,ExtractLHOF(V,P)); 8 | V.CurrentTime = 0; 9 | shiAll = cat(2,shiAll,ExtractLLBP(V,P)); 10 | videoFeature = shiAll; 11 | 12 | end 13 | 14 | -------------------------------------------------------------------------------- /Test1/Modified_LBP.m: -------------------------------------------------------------------------------- 1 | function result = Modified_LBP(IM) 2 | % The function computes the Modified LBP for an image 3 | % according to the method described in "Multi-Resolution 4 | % Local Binary Patterns or Image Classification" by Peng Liang, 5 | % Shao-Fa Li and Jiang-Wei. It takes the image as the input and 6 | % for each pixel of the image it takes the difference of the neighbouring 7 | % pixels which are opposite to each other. It consideres 8 neighbouring 8 | % pixels for the central pixel. 9 | 10 | 11 | CROPPED_IM = IM(2:end-1,2:end-1); 12 | 13 | % The neighbour matrices are seperated 14 | G0_IM = IM(2:end-1,3:end); 15 | G1_IM = IM(3:end,3:end); 16 | G2_IM = IM(3:end,2:end-1); 17 | G3_IM = IM(3:end,1:end-2); 18 | G4_IM = IM(2:end-1,1:end-2); 19 | G5_IM = IM(1:end-2,1:end-2); 20 | G6_IM = IM(1:end-2,2:end-1); 21 | G7_IM = IM(1:end-2,3:end); 22 | 23 | 24 | 25 | % The difference between the neighbours is found out 26 | D1 = G0_IM >= G4_IM; 27 | D2 = G1_IM >= G5_IM; 28 | D3 = G2_IM >= G6_IM; 29 | D4 = G3_IM >= G7_IM; 30 | D5 = (G0_IM + G1_IM + G2_IM + G3_IM + G4_IM + G5_IM + G6_IM + G7_IM)/8 >= CROPPED_IM; 31 | 32 | 33 | % The final map is computed 34 | map = D1 + 2.*D2 + 4.*D3 + 8.*D4 + 16.*D5; 35 | 36 | 37 | neighbour = 8; 38 | nbins = 2^((neighbour/2)+1); 39 | result=hist(map(:),0:(nbins-1)); 40 | 41 | % normalize 42 | result = result/(eps+sum(result)); 43 | end 44 | -------------------------------------------------------------------------------- /Test1/SequentialData.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mavenzer/Player-Action-Detection/88952dc5b97a4487376403d191da7835a458dd7e/Test1/SequentialData.mat -------------------------------------------------------------------------------- /Test1/ShuffleData.m: -------------------------------------------------------------------------------- 1 | function [ SDS ] = ShuffleData( DS ) 2 | %Input : DS -> Sequential DataSet 3 | %Output : SDS -> Shuffled Dataset 4 | 5 | numofobs=length(DS.Data); 6 | rearrangement= randperm(numofobs); 7 | SDS.Data=DS.Data(rearrangement,:); 8 | SDS.Labelidx=DS.Labelidx(rearrangement); 9 | SDS.Label = DS.Label; 10 | 11 | end 12 | 13 | -------------------------------------------------------------------------------- /Test1/calFuzzyHist.m: -------------------------------------------------------------------------------- 1 | function coarseHist1 = calFuzzyHist(flow,param) 2 | % Computing Fuzzy Histogram of Optical Flow Orientations 3 | % 4 | % Input : flow - opticalFlow Object for storing optical flow field 5 | % param - parameters for computing FHOFO/FHOOF 6 | % param.feature : {'fhofo','fhoof'} 7 | % param.n_1 : number of fine histogram bins 8 | % param.n_2 : number of coarse histogram bins 9 | % param.sigma : variance used in memebrship function 10 | % Output: coarseHist - the resulting coarse histogram after feature extraction 11 | 12 | 13 | if nargin == 1 14 | param.feature = 'fhofo'; 15 | param.n_1 = 50; 16 | param.n_2 = 18; 17 | param.sigma = 10; 18 | end 19 | n_1 = param.n_1; 20 | n_2 = param.n_2; 21 | sigma = param.sigma; 22 | 23 | % get the index of pixels having zero magnitude flow and remove them 24 | flowOri = flow.Orientation; 25 | flowOri(flow.Magnitude==0) = NaN; 26 | if strcmpi(param.feature,'FHOOF') 27 | % gettting the weighted histogram for FHOOF 28 | intervals = linspace(-pi,pi,n_1+1); 29 | pixelsProcessed = false(size(flow.Magnitude)); 30 | fine_hist = zeros(1,n_1); 31 | for v = 2:length(intervals) 32 | idx = (flowOri <= intervals(v)) & ~pixelsProcessed; 33 | fine_hist(v-1) = sum(flow.Magnitude(:).*idx(:)); 34 | pixelsProcessed = pixelsProcessed | idx; 35 | end 36 | fineHist = fine_hist/sum(fine_hist); 37 | elseif strcmpi(param.feature,'FHOFO') 38 | % get the fine histogram vector (n_1 bins) for FHOFO 39 | binCenters = linspace(-pi,pi,n_1+1); 40 | binCenters = binCenters+(binCenters(2)-binCenters(1))/2; 41 | binCenters = binCenters(1:numel(binCenters)-1); 42 | fineHist = hist(flowOri(:),binCenters); 43 | else 44 | error('The feature should be one of these: FHOFO,FHOOF') 45 | end 46 | %%% create the fine to coarse mapping matrix 47 | M2 = hist_fine2coarseMapping(n_1,n_2,sigma); 48 | %%% for improving speed construct M2 in the main program and uncomment the 49 | %%% following 2 lines and comment the above line 50 | % global M2 51 | % if isempty(M2), M2 = hist_fine2coarseMapping(n_1,n_2,sigma); end 52 | 53 | % calculate the coarse histogram 54 | coarseHist = M2*fineHist'; 55 | 56 | % normalize 57 | coarseHist = coarseHist/(eps+sum(coarseHist)); 58 | coarseHist1 = coarseHist'; 59 | end 60 | 61 | -------------------------------------------------------------------------------- /Test1/hist_fine2coarseMapping.m: -------------------------------------------------------------------------------- 1 | function M2 = hist_fine2coarseMapping(n_1,n_2,sigma) 2 | % this function creates a matrix that maps the fine histogram to a coarse one 3 | % 4 | % The folowing input arguments should be passed to the function 5 | % n_1 = 36; %length histogram of fine orientation grids 6 | % n_2 = 10; %length histogram of coarse orientation grids 7 | % sigma = 10; % variance of gaussian Membership function 8 | % 9 | % Usage: 10 | % M2 = hist_fine2coarseMapping(36,10,2); 11 | % coarseHistogram = M2*fineHistogram; 12 | 13 | x = 1:n_1; 14 | center = round(n_1/2); 15 | mf = evalmf(x, [sigma,center], 'gaussmf'); 16 | % mf = evalmf(x, mf2mf([sigma,center],'gaussmf','trimf'), 'trimf'); 17 | % mf = evalmf(x, mf2mf([sigma,center],'gaussmf','trapmf'), 'trapmf'); 18 | % mf = evalmf(x, mf2mf([sigma,center],'gaussmf','gbellmf'), 'gbellmf'); 19 | 20 | mf1 = circshift(mf',center); 21 | M1 = []; 22 | for i = 1:n_1 23 | M1 = [M1, mf1]; 24 | mf1 = circshift(mf1, 1); 25 | end 26 | 27 | M2 = M1(round(linspace(1,n_1,n_2)),:); 28 | end 29 | -------------------------------------------------------------------------------- /Test2/Demo.m: -------------------------------------------------------------------------------- 1 | clear; clc; 2 | V = VideoReader('Run (1).mp4'); 3 | % FV(1,:) = ExtractGLBP(V); 4 | FV(1,:) = ExtractLLBP(V); 5 | -------------------------------------------------------------------------------- /Test2/ExtractGLBP.m: -------------------------------------------------------------------------------- 1 | function [ videoFeature ] = ExtractGLBP( V ) 2 | % Input: V -> VideoReader object for the video 3 | % Output: videoFeature -> LBP feature vector 4 | 5 | for i = 1:25 6 | videoVol(:,:,i) = rgb2gray(readFrame(V)); 7 | end 8 | shiAll = []; % the variable that contains all the features concatenated for all the frames. 9 | for v2 = 1:size(videoVol,3) % for each frame 10 | LBPFeature = Modified_LBP(videoVol(:,:,v2)); % compute feature vector for each frame 11 | % shiAll = cat(2,shiAll,LBPFeature); % concatenate the features for all frames 12 | if v2 == 1 13 | shiAll = LBPFeature; 14 | else 15 | shiAll = shiAll + LBPFeature; 16 | end 17 | end 18 | videoFeature = shiAll; % the feture that describes the LBP in the video 19 | 20 | end 21 | 22 | -------------------------------------------------------------------------------- /Test2/ExtractLLBP.m: -------------------------------------------------------------------------------- 1 | function [ videoFeature ] = ExtractLLBP( V ) 2 | % Input: V -> VideoReader object for the video 3 | % Output: videoFeature -> LBP feature vector 4 | 5 | for i = 1:25 6 | videoVol(:,:,i) = rgb2gray(readFrame(V)); 7 | end 8 | shiAll = []; % the variable that contains all the features concatenated for all the frames. 9 | for v2 = 1:size(videoVol,3) % for each frame 10 | 11 | % compute feature vector for each frame parts 12 | LBPFeature = Modified_LBP(videoVol(1:50,1:50,v2)); 13 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(1:50,51:100,v2))); 14 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(1:50,101:150,v2))); 15 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(51:100,1:50,v2))); 16 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(51:100,51:100,v2))); 17 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(51:100,101:150,v2))); 18 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(101:150,1:50,v2))); 19 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(101:150,51:100,v2))); 20 | LBPFeature = cat(2,LBPFeature,Modified_LBP(videoVol(101:150,101:150,v2))); 21 | % shiAll = cat(2,shiAll,LBPFeature); % concatenate the features for all frames 22 | if v2 == 1 23 | shiAll = LBPFeature; 24 | else 25 | shiAll = shiAll + LBPFeature; 26 | end 27 | end 28 | videoFeature = shiAll; % the feture that describes the LBP in the video 29 | 30 | end 31 | -------------------------------------------------------------------------------- /Test2/Modified_LBP.m: -------------------------------------------------------------------------------- 1 | function result = Modified_LBP(IM) 2 | % The function computes the Modified LBP for an image 3 | % according to the method described in "Multi-Resolution 4 | % Local Binary Patterns or Image Classification" by Peng Liang, 5 | % Shao-Fa Li and Jiang-Wei. It takes the image as the input and 6 | % for each pixel of the image it takes the difference of the neighbouring 7 | % pixels which are opposite to each other. It consideres 8 neighbouring 8 | % pixels for the central pixel. 9 | 10 | 11 | CROPPED_IM = IM(2:end-1,2:end-1); 12 | 13 | % The neighbour matrices are seperated 14 | G0_IM = IM(2:end-1,3:end); 15 | G1_IM = IM(3:end,3:end); 16 | G2_IM = IM(3:end,2:end-1); 17 | G3_IM = IM(3:end,1:end-2); 18 | G4_IM = IM(2:end-1,1:end-2); 19 | G5_IM = IM(1:end-2,1:end-2); 20 | G6_IM = IM(1:end-2,2:end-1); 21 | G7_IM = IM(1:end-2,3:end); 22 | 23 | 24 | 25 | % The difference between the neighbours is found out 26 | D1 = G0_IM >= G4_IM; 27 | D2 = G1_IM >= G5_IM; 28 | D3 = G2_IM >= G6_IM; 29 | D4 = G3_IM >= G7_IM; 30 | D5 = (G0_IM + G1_IM + G2_IM + G3_IM + G4_IM + G5_IM + G6_IM + G7_IM)/8 >= CROPPED_IM; 31 | 32 | 33 | % The final map is computed 34 | map = D1 + 2.*D2 + 4.*D3 + 8.*D4 + 16.*D5; 35 | 36 | 37 | neighbour = 8; 38 | nbins = 2^((neighbour/2)+1); 39 | result=hist(map(:),0:(nbins-1)); 40 | 41 | % normalize 42 | result = result/(eps+sum(result)); 43 | end 44 | 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