1 / 33

Human Detection

Human Detection . Mikel Rodriguez. Organization. 1. Moving Target Indicator (MTI) Background models Moving region detection Target chip generation Results. 2. Target Classification (Human Detection) Target features Support vector machines Results. Input Frame. Object Detection.

blue
Download Presentation

Human Detection

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Human Detection Mikel Rodriguez

  2. Organization 1. Moving Target Indicator (MTI) • Background models • Moving region detection • Target chip generation • Results 2. Target Classification (Human Detection) • Target features • Support vector machines • Results Input Frame Object Detection Target Chips Wavelet Features SVM Classifier MTI Classification

  3. Moving Target Indicator Moving target indicator (MTI) identifies moving objects which can be potential targets

  4. MTI Motivation • Becoming increasingly important in military and civilian applications • To minimize human involvement • Expensive • Short attention spans • Computerized monitoring system • Real-time capability • 24/7

  5. MTI Challenges • Different sensor modalities • LADAR, IR, EO • Targets with different dynamics • Small targets • Weather conditions • Illumination changes, shadows…

  6. Background Modeling Moving Target Detection Intensity models Gradient models Background Subtraction Targets Chips Position MTI Input Video dynamic update

  7. Hierarchical Approach to Background Modeling • Pixel level • Region level • Frame level

  8. Pixel LevelBackground Features • Intensity, heat index • Gradient • 2D: magnitude, orientation EO IR Magnitude Orientation

  9. Pixel LevelBackground Features • Intensity, heat index • Per-pixel mixture of Gaussians. • Gradient based subtraction • Gradient feature vector =[m, dd]

  10. Pixel LevelMoving Region Detection • Mark pixels that are different from the background intensity model • Mark pixels that are different from the background gradient model

  11. Gradient Image Color based Final Region LevelFusion of Intensity & Gradient Results • For each color based region, presence of“edge difference” pixels at the boundaries is checked. • Regions with small number of edge difference pixel are removed, color model is updated.

  12. Frame LevelModel Update • Performs a high level analysis of the scene components If more > 50% of the intensity based background subtracted image becomes foreground. Frame level processing issues an alert Intensity based subtraction results are ignored

  13. MTI Background Object Chips ConnectedComponents() BoundaryEdges() SetNumGaussians() SetAlpha() SetRhoMean() SetWeightThresh() SetActiveRegion() GetNumGaussians() GetAlpha() GetRhoMean() GetWeightThresh() GetActiveRegion() SetBoundingBox() SetRhoLocation() SetCentroid() GetBoundingBox() GetRhoLocation() GetCentroid() IsFalseDetection() Centroid() ObjectArea() Height() Width() Structure of the MTI Class

  14. Results

  15. Target Classification Classification of objects into two classes: humans and others, from target chips generated by MTI

  16. Challenges • Small size • Obscured targets • Background clutter • Weather conditions

  17. Training SVM Feature Extraction Wavelet MTI Chips Classifier Flow Negative Positive Support Vectors Testing Decision

  18. Blurred Vertical Horizontal Diagonal Wavelet Based Target Features

  19. Feature Extraction • Apply 2D Wavelet Transform • Daubechies wavelets • Apply Inverse 2D Wavelet Transform to each of the coefficient matrices individually • Rescale and vectorize output matrices

  20. Why Wavelets? • Separability among samples • Humans can be separated from cars and background Correlation using gray levels Correlation using gradient mag.

  21. Why Wavelets? Person 11 - DB3 Wavelet Correlation

  22. Support Vector Machines (SVM) • Classification of data into two classes • N dimensional data. • Linearly separable • If not transform data into a higher dimensional space • Find separating N dimensional hyperplane

  23. SVMLinear Classifier hyperplane equation N dimensional data point xi Sample distance to hyperplane

  24. SVMBest Hyperplane? • Infinite number of hyperplanes. • Minimize ri over sample set xi • Maximize margin  around hyperplane • Samples inside the margin are the support vectors

  25. SVMTraining Set • Let  =1,A training set is a set of tuples: {(x1,y1),(x2,y2),…(xm,ym)}. • For support vectors inequality becomes equality • Unknowns are w and b

  26. SVMLinear Separability • Linear programming, • Separator line in 2D w1xi,1+w2xi,2+b=0. • Find w1,w2, b such that is maximized • Find w1,w2, b such that (w)=wTw is minimized

  27. SVMSolution • Has the following form: • Non-zero i indicates xi is support vector • Classifying function is:

  28. Human Classification TrainingFunction TestingFunction ReadPositiveImages() ReadNegativeImages() AssemblePositive() AssembleNegative() AssembleMatrices() LoadSVM() ReadImages() ExtractFeatures ConvertToGray() ApplyWaveletFilter() ApplyInverseTrans() ResizeInverse() VectorizeInverse() Concatenate() TrainSVM TestSVM LIBSVM LIBSVM Classification Class

  29. Classification Baseline Analysis • Run time for 3.0GHz dualcore, 2GB RAM • Training: 276 training samples 8.015 seconds • Testing: 24.087 chips (25 by 25) per second • Classifier size • Depends on diversity of images • For 276 training samples of 25x25, classifier size is 1.101 MB

  30. Classification Baseline Analysis • Memory requirements • Requires entire set of support vectors • Current classifier

  31. Experiments Vivid Dataset UCF Dataset

  32. Results • Training set • 300 target chips • Testing • 3872 human chips • 5605 vehicle and background chips • Performance • 2.4% false positive (others classified as pedestrians) • 3.2% false negative (pedestrian classified as others)

  33. Future directions • MTI • Detection by parts • Motion clustering • Classification • Various kernels for SVM • Better target features • Motion, steerable pyramids, shape features (height, width) • Local wavelet coefficients • Adaboost

More Related