Week 6 University of Nevada, Reno

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Presentation transcript:

Week 6 University of Nevada, Reno Emily Hand Week 6 University of Nevada, Reno

GML Adaboost Not Good Not moving forward with this approach The length of the feature vector must be less than the number of samples. 101-200 Samples Features: HOG, LBP, 3DHistogram :( We tried different features, but nothing good came from it. Way too many false positives

False Positives

OpenTLD Detector Extracting their detector much more complicated than we thought. No documentation A lot of pre-compiled mex files Having issues with those

OpenTLD Detector Extracted their features :) They resize each patch to [15 15] and then they reshape the patch to a column vector and normalize to zero mean unit variance (ZMUV). ZMUV Remove mean intensity value from an image and scale it with its variance

SVM Progress Histogram of Oriented Gradients (HOG) Local Binary Pattern (LBP) 3D Histogram

Regular Histogram

More useful information 3D Histogram B G R 8x8x8 Bins More useful information

Some Results

Some Results

Next Week Motion Features Partial Occlusions Scale Changes