Detection III: Analyzing and Debugging Detection Methods
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1 CS 1699: Intro to Computer Vision Detection III: Analyzing and Debugging Detection Methods Prof. Adriana Kovashka University of Pittsburgh November 17, 2015
2 Today Review: Deformable part models How can we speed up detection? In what ways does detection fail? How can we visualize features and models?
3 Parts-based Models Define object by collection of parts modeled by 1. Appearance 2. Spatial configuration Rob Fergus
4 How to model spatial relations? Star-shaped model X = X Part Part Part X Root Part Part Derek Hoiem
5 Implicit shape models: Training 1. Build vocabulary of patches around extracted interest points using clustering 2. Map the patch around each interest point to closest word 3. For each word, store all positions it was found, relative to object center Lana Lazebnik
6 Implicit shape models: Testing 1. Given new test image, extract patches, match to vocabulary words 2. Cast votes for possible positions of object center 3. Search for maxima in voting space Lana Lazebnik
7 Histograms of oriented gradients (HOG) Bin gradients from 8x8 pixel neighborhoods into 9 orientations (Dalal & Triggs CVPR 05)
8 Discriminative part-based models Root filter Part filters Deformation weights P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, Object Detection with Discriminatively Trained Part Based Models, PAMI 32(9), 2010 Lana Lazebnik
9 Scoring an object hypothesis The score of a hypothesis is the sum of appearance scores minus the sum of deformation costs Subwindow features n n i ( dxi, dyi, dxi, dyi i 0 i 1 score( p,..., pn) Fi H( pi ) D Displacements ) Appearance weights Deformation weights Adapted from Lana Lazebnik
10 What is an Object? B. Alexe, T. Deselaers, and V. Ferrari Computer Vision and Pattern Recognition (CVPR) 2010
11 Speeding up detection: Restrict set of windows we pass through SVM to those w/ high objectness Alexe et al., CVPR 2010
12 Alexe et al., CVPR 2010 Objectness cue #1: Where people look
13 Objectness cue #2: color contrast at boundary Alexe et al., CVPR 2010
14 Objectness cue #3: no segments straddling the object box Alexe et al., CVPR 2010
15 Boxes found to have high objectness Cyan = ground truth bounding boxes, yellow = correct and red = incorrect predictions for objectness Only run the sheep / horse / chair etc. classifier on the yellow/red boxes. Alexe et al., CVPR 2010
16 Today Review: Deformable part models How can we speed up detection? In what ways does detection fail? How can we visualize features and detections?
17 Diagnosing Error in Object Detectors D. Hoiem, Y. Chodpathumwan and Q. Dai European Conference on Computer Vision (ECCV) 2012
18 Object detection is a collection of problems Intra-class Variation for Airplane Occlusion Shape Viewpoint Distance Hoiem et al., ECCV 2012
19 Object detection is a collection of problems Confusing Distractors for Airplane Background Similar Categories Dissimilar Categories Localization Error Hoiem et al., ECCV 2012
20 Top false positives: Airplane (DPM) AP = Background 27% Localization 29% Other Objects 11% Similar Objects 33% Bird, Boat, Car Hoiem et al., ECCV 2012
21 Top false positives: Dog (DPM) AP = Background 23% Localization 17% Other Objects 10% Similar Objects 50% Person, Cat, Horse Hoiem et al., ECCV 2012
22 Analysis of object characteristics Additional annotations for seven categories: occlusion level, parts visible, sides visible Hoiem et al., ECCV 2012
23 Object characteristics: Aeroplane Occlusion: poor robustness to occlusion, but little impact on overall performance Easier (None) Hoiem et al., ECCV 2012 Harder (Heavy)
24 Object characteristics: Aeroplane Size: strong preference for average to above average sized airplanes Large Medium X-Large Small X-Small Easier Hoiem et al., ECCV 2012 Harder
25 Object characteristics: Aeroplane Aspect Ratio: 2-3x better at detecting wide (side) views than tall views X-Wide Wide Medium X-Tall Tall Easier (Wide) Hoiem et al., ECCV 2012 Harder (Tall)
26 Object characteristics: Aeroplane Sides/Parts: best performance = direct side view with all parts visible Easier (Side) Hoiem et al., ECCV 2012 Harder (Non-Side)
27 Conclusions Most errors that detectors make are reasonable Localization error and confusion with similar objects Misdetection of occluded or small objects Detectors have different sensitivity to different factors E.g. less sensitive to truncation than to size differences Code and annotations are available online Adapted from Hoiem et al., ECCV 2012
28 Today Review: Deformable part models How can we speed up detection? In what ways does detection fail? How can we visualize features and detections?
29 HOGgles: Visualizing Object Detection Features C. Vondrick, A. Khosla, T. Malisiewicz, and A. Torralba International Conference on Computer Vision (ICCV) 2013
30 Why did the detector fail? Car Vondrick et al., ICCV 2013
31 Vondrick et al., ICCV 2013 What information is lost?
32 Vondrick et al., ICCV 2013 What information is lost?
33 Recovering image from neighbors Image HOG Top detections Vondrick et al., ICCV 2013
34 Recovering image from neighbors Image HOG Top detections Vondrick et al., ICCV 2013
35 Recovering image from neighbors Image HOG Top detections Vondrick et al., ICCV 2013
36 Recovering image from neighbors Image HOG Top detections Vondrick et al., ICCV 2013
37 Better recovery using paired dictionary Vondrick et al., ICCV 2013
38 A microscope to view HOG 2x more intuitive Vondrick et al., ICCV 2013
39 vs Human Vision HOG Vision Vondrick et al., ICCV 2013
40 Vondrick et al., ICCV 2013
41 Vondrick et al., ICCV 2013
42 Vondrick et al., ICCV 2013
43 Vondrick et al., ICCV 2013
44 Vondrick et al., ICCV 2013
45 Vondrick et al., ICCV 2013
46 The HOGgles Challenge Humans detect & DPMs detect Vondrick et al., ICCV 2013
47 The HOGgles Challenge Humans miss & DPM miss Vondrick et al., ICCV 2013
48 Vondrick et al., ICCV 2013 Chair Detections
49 Vondrick et al., ICCV 2013 Chair Detections
50 Vondrick et al., ICCV 2013 Car Detections
51 Vondrick et al., ICCV 2013 Car Detections
52 Precision HOG+Human Human performance with HOG is poor despite perfect learning Detector Chair Loss due to RGB -> HOG HOG+DPM HOG+Human RGB+Human Recall Vondrick et al., ICCV 2013
53 Why did the detector fail? Car Vondrick et al., ICCV 2013
54 Why did the detector fail? Car Vondrick et al., ICCV 2013
55 Why did the detector fail? Car Vondrick et al., ICCV 2013
56 Visualizing Learned Models Car Person Bottle Bicycle Motorbike Chair TV Horse Vondrick et al., ICCV 2013
57 What is this?
58 What is this?
59 What is this?
60 What is this?
61 What is this?
62 What is this?
63 Summary We can speed up object detection by using the notion of objectness to prune windows unlikely to contain any object Some failure modes are more important than others and fixing them could increase the overall detection performance Even humans cannot produce correct classifications with imperfect features
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