Visuelle Perzeption für Mensch- Maschine Schnittstellen
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1 Visuelle Perzeption für Mensch- Maschine Schnittstellen Vorlesung, WS 2008 Dr. Rainer Stiefelhagen Dr. Edgar Seemann Interactive Systems Laboratories Universität Karlsruhe (TH) Edgar Seemann,
2 Programming Assignments WS 2008/09 Dr. Edgar Seemann Edgar Seemann,
3 Organisatorisches There will be no lecture on Friday, January 23 rd Edgar Seemann,
4 Assignment 1 Results Computer Vision for Human-Computer Interaction Research Group, Universität Karlsruhe (TH) cv:hci Gruppe 1: Christian Johner Mike Morante Patrick Mehl Gruppe 2: Steffen Braun Gruppe 10: Thomas Stephan Gruppe 11: Igor Plotkin Gruppe 3: Martin Wagner Hilke Kieritz Jan Hendrik Hammer Gruppe 4: Wenlei Wu Chengchao Qu Gruppe 5: Michael Weber Tomas Semela Dennis Kopcan Gruppe 6: Johann Korndoerfer Daniel Koester Daniel Putsch Gruppe 8 Mathias Luedtke (Florian Krupicka) Gruppe 9 Felix Reuter Elke Mueller Gruppe 7 Benjamin Bartosch Thomas Lichtenstein Edgar Seemann,
5 Student presentations This Lecture Short Intro into Assignment 3 Data Set Choice of Parameters Non-Maxiumum Suppression Edgar Seemann,
6 It s your turn Edgar Seemann, cv:hci Computer Vision for Human-Computer Interaction Research Group, Universität Karlsruhe (TH)
7 Assignment 3 Edgar Seemann, cv:hci Computer Vision for Human-Computer Interaction Research Group, Universität Karlsruhe (TH)
8 Assignment 3 People Detection Detect for the given images, where and at which scale the image contains people That is: 1. We have to implement a sliding window search 2. At each location we classify the window with the SVM from assignment 2 3. We have to fuse the detections with a non-maxium suppression approach Edgar Seemann,
9 The Same Training Data Training Set (PersonTrain.tar.bz2): 2418 positive examples 2436 negative examples 96x160 pixels (64x128 + larger border) Idl-files: Pos.idl Neg.idl Edgar Seemann,
10 Test Set Test set (PersonTestDetection.tar.bz2): 41 positive images 0 negative images Ground-truth defined in testset.idl Edgar Seemann,
11 Quantitative Evaluation Computer Vision for Human-Computer Interaction Research Group, Universität Karlsruhe (TH) cv:hci We use a precision-recall c++ binary ( precisionrecall ) A python script just sets some default commandline parameters./doroc.py groundtruth.idl result.idl Produces a text file result.txt containing the plotting data plotsimple.py can be used to display the results Edgar Seemann,
12 Your Task Compute an.idl file, which specifies for each test image a set of detection hypotheses Annotool helps to display results at different confidence levels Edgar Seemann,
13 Sliding Window Technique Red: score>0.8 We obtain for each position/scale a recognition score Parameters: scale range, scale steps, x/y-steps Positions with low scores can be discarded Edgar Seemann,
14 Sliding Window Parameters Use 64x128 windows Start at original resolution Shift windows 4 pixels in x-direction 4 pixels in y-direction You free to experiment with these values Change scale Shrink image with a factor of 1.2 Other common choice is sqrt(2) as shrinking factor Edgar Seemann,
15 Discard bad hypotheses We have to find a reasonable threshold for the SVM score Suggestion: Accept everything with score >0 Display results in AnnoTool Decrease/Increase threshold according to visual results Rules: Allow enough hypotheses to have a recall of 1 We should have multiple hypotheses (shifted, scaled) around each person Edgar Seemann,
16 Non-Maximum Suppression A good detector will generally not only fire on the exact position Need to reduce the number of detections, since every additional detection (even on the object) will count as false positive detection Edgar Seemann,
17 Naïve Approach Pick hypotheses in a greedy fashion Accept the strongest hypothesis Remove all other hypotheses, which strongly overlap libannotation contains methods to compute the cover/overlap between two hypotheses Edgar Seemann,
18 Non-Maxiumum suppression approaches Finding modes in a non-parametric distribution Kernel Density Estimation Mean-Shift Mode Estimation (MSME) (e.g. [Dalal 05]) Pixel-based reasoning (e.g. [Leibe et al. 2004]) Infer an object segmentation Use segmentation to determine, which pixels belong to which object Edgar Seemann,
19 Mean Shift Theory Edgar Seemann,
20 Kernel Density Estimation Which probability at position x should be higher? x x Single peak could result from noise, image artifacts etc. Edgar Seemann,
21 Intuitive Description Region of interest Center of mass Objective : Find the densest region Distribution of identical billiard balls Mean Shift vector Edgar Seemann,
22 Intuitive Description Region of interest Center of mass Objective : Find the densest region Distribution of identical billiard balls Mean Shift vector Edgar Seemann,
23 Intuitive Description Region of interest Center of mass Objective : Find the densest region Distribution of identical billiard balls Mean Shift vector Edgar Seemann,
24 Intuitive Description Region of interest Center of mass Objective : Find the densest region Distribution of identical billiard balls Mean Shift vector Edgar Seemann,
25 Intuitive Description Region of interest Center of mass Objective : Find the densest region Distribution of identical billiard balls Mean Shift vector Edgar Seemann,
26 Intuitive Description Region of interest Center of mass Objective : Find the densest region Distribution of identical billiard balls Mean Shift vector Edgar Seemann,
27 Intuitive Description Region of interest Center of mass Objective : Find the densest region Distribution of identical billiard balls Edgar Seemann,
28 Mean Shift Properties Automatic convergence speed the mean shift vector size depends on the gradient itself. Near maxima, the steps are small and refined Adaptive Gradient Ascent Convergence is guaranteed for infinitesimal steps only infinitely convergent, (therefore set a lower bound) For Uniform Kernel ( ), convergence is achieved in a finite number of steps Normal Kernel ( ) exhibits a smooth trajectory, but is slower than Uniform Kernel ( ). Edgar Seemann,
29 Real Modality Analysis Tessellate the space with windows Run the procedure in parallel Edgar Seemann,
30 Real Modality Analysis An example Window tracks signify the steepest ascent directions Edgar Seemann,
31 Mean Shift Strengths & Weaknesses Strengths : Application independent tool Suitable for real data analysis Does not assume any prior shape (e.g. elliptical) on data clusters Can handle arbitrary feature spaces Weaknesses : The window size (bandwidth selection) is not trivial Inappropriate window size can cause modes to be merged, or generate additional shallow modes Use adaptive window size Only ONE parameter to choose h (window size) has a physical meaning, unlike K-Means Edgar Seemann,
32 Presentation Shortly present what exactly you have implemented (maybe a visualization of your features) What were the lessons learned? Please prepare a couple of slides Try to finish within the given 8 minutes Send me your PPT (<=PPT2003) or PDF files till February 6th, 8 am Edgar Seemann,
33 Send me your results Please send me your results: Group members (Name, MatrikelNr.) Presentation file Source code I should be able to run the code and reproduce the results seemann@pedestrian-detection.com If the is larger than 10mb, please try to split it I will try to give some feedback Edgar Seemann,
34 End of Lecture Edgar Seemann,
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