Object Category Detection. Slides mostly from Derek Hoiem

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1 Object Category Detection Slides mostly from Derek Hoiem

2 Today s class: Object Category Detection Overview of object category detection Statistical template matching with sliding window Part-based Models

3 Object Category Detection Focus on object search: Where is it? Build templates that quickly differentiate object patch from background patch Dog Model Object or Non-Object?

4 Challenges in modeling the object class Illumination Object pose Clutter Occlusions Intra-class appearance Viewpoint Slide from K. Grauman, B. Leibe

5 Challenges in modeling the non-object class True Detections Bad Localization Confused with Similar Object Misc. Background Confused with Dissimilar Objects

6 General Process of Object Recognition Specify Object Model What are the object parameters? Generate Hypotheses Score Hypotheses Resolve Detections

7 Specifying an object model 1. Statistical Template in Bounding Box Object is some (x,y,w,h) in image Features defined wrt bounding box coordinates Image Template Visualization Images from Felzenszwalb

8 Specifying an object model 2. Articulated parts model Object is configuration of parts Each part is detectable Images from Felzenszwalb

9 Specifying an object model 3. Hybrid template/parts model Detections Template Visualization Felzenszwalb et al. 2008

10 Specifying an object model 3. Hybrid template/parts model Detections Template Visualization Felzenszwalb et al. 2008

11 General Process of Object Recognition Specify Object Model Generate Hypotheses Propose an alignment of the model to the image Score Hypotheses Resolve Detections

12 Generating hypotheses 1. Sliding window Test patch at each location and scale

13 Generating hypotheses 1. Sliding window Test patch at each location and scale

14 Generating hypotheses 2. Voting from patches/keypoints Interest Points Matched Codebook Entries Probabilistic Voting y s 3D Voting Space (continuous) x ISM model by Leibe et al.

15 General Process of Object Recognition Specify Object Model Generate Hypotheses Score Hypotheses Gradient based features for template Spring energies for part-based models Resolve Detections

16 General Process of Object Recognition Specify Object Model Generate Hypotheses Score Hypotheses Resolve Detections Rescore each proposed object based on whole set

17 Resolving detection scores 1. Non-max suppression Score = 0.8 Score = 0.8 Score = 0.1

18 Resolving detection scores 2. Context/reasoning meters Hoiem et al meters

19 General Process of Object Recognition Specify Object Model Generate Hypotheses Score Hypotheses Resolve Detections

20 Design challenges How to efficiently search for likely objects Even simple models require searching hundreds of thousands of positions and scales Feature design and scoring How should appearance be modeled? What features correspond to the object? How to deal with different viewpoints? Often train different models for a few different viewpoints Implementation details Window size Aspect ratio Translation/scale step size Non-maxima suppression

21 Example: Dalal-Triggs pedestrian detector 1. Extract fixed-sized (64x128 pixel) window at each position and scale 2. Compute HOG (histogram of gradient) features within each window 3. Score the window with a linear SVM classifier 4. Perform non-maxima suppression to remove overlapping detections with lower scores Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

22 Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

23 Tested with RGB LAB Grayscale Gamma Normalization and Compression Square root Log Slightly better performance vs. grayscale Very slightly better performance vs. no adjustment

24 Outperforms centered diagonal uncentered cubic-corrected Sobel Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

25 Histogram of gradient orientations Orientation: 9 bins (for unsigned angles) Histograms in 8x8 pixel cells Slides by Pete Barnum Votes weighted by magnitude Bilinear interpolation between cells Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

26 Normalize with respect to surrounding cells Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

27 # orientations X= # features = 15 x 7 x 9 x 4 = 3780 # cells # normalizations by neighboring cells Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

28 pos w neg w Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

29 pedestrian Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

30 Detection examples

31

32 When do statistical templates make sense? Caltech 101 Average Object Images

33 Previously Template Matching Training Image Close Match Not close Template Matching

34 Part-based model Articulated parts model Object is configuration of parts Each part is detectable Images from Felzenszwalb

35 Deformable objects Images from Caltech-256 Slide Credit: Duan Tran

36 Deformable objects Images from D. Ramanan s dataset Slide Credit: Duan Tran

37 Compositional objects

38 Parts-based Models Define object by collection of parts modeled by 1. Appearance 2. Spatial configuration Slide credit: Rob Fergus

39 How to model spatial relations? One extreme: fixed template

40 How to model spatial relations? Another extreme: no constraint (bag of words) =

41 How to model spatial relations? Star-shaped model Part Part Part Root Part Part

42 How to model spatial relations? Star-shaped model X = X Part X Part Root Part Part Part

43 How to model spatial relations? Tree-shaped model

44 How to model spatial relations? Many others... O(N 6 ) O(N 2 ) O(N 3 ) O(N 2 ) Fergus et al. 03 Fei-Fei et al. 03 Leibe et al. 04, 08 Crandall et al. 05 Fergus et al. 05 Crandall et al. 05 Felzenszwalb & Huttenlocher 05 Csurka 04 Vasconcelos 00 Bouchard & Triggs 05 Carneiro & Lowe 06 from [Carneiro & Lowe, ECCV 06]

45 We will see the first one Part 1. Star-shaped model Example: ISM Leibe et al. 2004, 2008 Part Part Root Part Part 2. Tree-shaped model Example: Pictorial structures Felzenszwalb Huttenlocher 2005

46 ISM: Implicit Shape Model Training overview Start with bounding boxes and (ideally) segmentations of objects

47 ISM: Implicit Shape Model Training overview Start with bounding boxes and (ideally) segmentations of objects Extract local features (e.g., patches or SIFT) at interest points on objects

48 ISM: Implicit Shape Model Training overview Start with bounding boxes and (ideally) segmentations of objects Extract local features (e.g., patches or SIFT) at interest points on objects Cluster features to create codebook

49 ISM: Implicit Shape Model Training overview Start with bounding boxes and (ideally) segmentations of objects Extract local features (e.g., patches or SIFT) at interest points on objects Cluster features to create codebook Record relative bounding box and segmentation for each codeword

50 Codebook Representation Extraction of local object features Interest Points (e.g. Harris detector) Sparse representation of the object appearance Collect features from whole training set K. Grauman, B. Leibe

51 Appearance Codebook Clustering Results Visual similarity preserved Wheel parts, window corners, fenders,... Store cluster centers as Appearance Codebook K. Grauman, B. Leibe

52 Voting with Local Features For every feature, store possible occurrences Record relative size and scale of object For new image, let the matched features vote for possible object positions K. Grauman, B. Leibe

53 ISM: Implicit Shape Model Testing overview Extract interest points in test image

54 ISM: Implicit Shape Model Testing overview Extract interest points in test image Softly match to codebook entries

55 ISM: Implicit Shape Model Testing overview Extract interest points in test image Softly match to codebook entries Each matched codeword votes for object bounding box

56 ISM: Implicit Shape Model Testing overview Extract interest points in test image Softly match to codebook entries Each matched codeword votes for object bounding box Compute modes of votes using mean-shift

57 ISM: Implicit Shape Model Testing overview Extract interest points in test image Softly match to codebook entries Each matched codeword votes for object bounding box Compute modes of votes using mean-shift Check which codewords voted for modes

58 ISM: Implicit Shape Model Testing overview Extract interest points in test image Softly match to codebook entries Each matched codeword votes for object bounding box Compute modes of votes using mean-shift Check which codewords voted for modes Refine

59 Example: Results on Cows K. Grauman, B. Leibe

60 Example: Results on Cows K. Grauman, B. Leibe

61 Example: Results on Cows K. Grauman, B. Leibe

62 Example: Results on Cows K. Grauman, B. Leibe

63 Example: Results on Cows K. Grauman, B. Leibe

64 Example: Results on Cows K. Grauman, B. Leibe

65 Example: Results on Cows K. Grauman, B. Leibe

66 ISM: Detection Results Qualitative Performance Robust to clutter, occlusion, noise, low contrast K. Grauman, B. Leibe

67 Beyond bounding boxes Interest Points Matched Codebook Entries Probabilistic Voting y s 3D Voting Space (continuous) x Backprojected codewords can vote: Pixel segmentation Part layout Pose Depth values Backprojected Hypotheses Backprojection of Maxima

68 ISM Top-Down Segmentation y s x K. Grauman, B. Leibe [Leibe04, Leibe08]

69 Example Results: Motorbikes K. Grauman, B. Leibe 69

70 Example Results: Chairs Dining room chairs Office chairs B. Leibe 70

71 Inferring Other Information: Part Labels Training Test Output [Thomas07] 71

72 Inferring Other Information: Part Labels (2) [Thomas07] 72

73 Inferring Other Information: Depth Maps [Thomas07] 73

74 Glimpse of the second one Part 1. Star-shaped model Example: ISM Leibe et al. 2004, 2008 Part Part Root Part Part 2. Tree-shaped model Example: Pictorial structures Felzenszwalb Huttenlocher 2005

75 Results for person matching 75

76 Results for person matching 76

77 Enhanced pictorial structures BMVC 2009

78 Things to remember Rather than searching for whole object, can locate parts that vote for object Better encoding of spatial variation These parts can vote for other things too Models can be broken down into part appearance and spatial configuration Wide variety of models Efficient optimization can be tricky but usually possible

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