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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