Diagnosing Error in Object Detectors
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1 Diagnosing Error in Object Detectors Derek Hoiem Yodsawalai Chodpathumwan Qieyun Dai (presented by Yuduo Wu) Most of the slides are from Derek Hoiem's ECCV 2012 presentation
2 Object detecion is a collecion of problems Intra-class Variation for Airplane Occlusion Shape Viewpoint Distance
3 Object detecion is a collecion of problems Background Confusing Distractors for Airplane Similar Categories Dissimilar Categories Localization Error
4 figs from Felzenszwalb et al How to evaluate object detectors? Detector analysis tool is important Average Precision (AP) Good summary staisic for quick comparison Not a good driver of research Typical evaluation through comparison of AP numbers Tools to evaluate detectors: where detectors fail and succeed potenial impact of paricular improvements
5 Detectors Analyzed as Examples on VOC 2007 Deformable Parts Model (DPM) Sliding window Mixture of HOG templates with latent HOG parts Multiple Kernel Learning (MKL) Jumping window Various spatial pyramid bag of words features combined with MKL x x x Felzenszwalb et al (v4) Vedaldi et al. 2009
6 Top false posi8ves: Airplane (DPM) AP = Background Localization 27% 29% 37 Other Objects 11% Similar Objects 33% 30 (Bird, Boat, Car) Impact of Removing/ Fixing FPs 7
7 Top false posi8ves: Dog (DPM) AP = Localization 17% Background 23% Other Objects 10% Similar Objects 50% (Person, Cat, Horse) 8 22 Impact of Removing/ Fixing FPs
8 Top false posi8ves: Dog (MKL) Other Objects 5% Background 4% Localization 17% AP = 0.17 Impact of Removing/ Fixing FPs Similar Objects 74% (Cow, Person, Sheep, Horse) Top 5 False Positives
9 Summary of False PosiIve Analysis DPM v4 (FGMR 2010) Biggest Improvement: Localization Error Small Improvement: Background Dissimilar Objects MKL (Vedaldi et al. 2009) MKL more reasonable than the DPM detectors
10 Analysis of object characteris8cs AddiIonal annotaions for seven categories: occlusion level, parts visible, sides visible Example of occlusion for aeroplane class
11 Normalized Average Precision Average precision is sensi8ve to number of posiive examples Normalized average precision: replace variable N j with fixed N Number of object examples in subset j
12 Object characterisics: Aeroplane
13 Object characterisics: Aeroplane Occlusion: poor robustness to occlusion, but little impact on overall performance Easier (None) Harder (Heavy)
14 Object characterisics: Aeroplane Size: strong preference for average to above average sized airplanes Large Medium X-Large Small X-Small Easier Harder
15 Object characterisics: Aeroplane Aspect Ratio: 2-3x better at detecting wide (side) views than tall views X-Wide Wide Medium X-Tall Tall Easier (Wide) Harder (Tall)
16 Object characterisics: Aeroplane Sides/Parts: best performance = direct side view with all parts visible Easier (Side) Harder (Non-Side)
17 Summarizing Detector Performance Avg. Performance of Best Case DPM (v4): Sensitivity and Impact Avg. Overall Performance Avg. Performance of Worst Case
18 Summarizing Detector Performance Best, Average, Worst Case DPM (FGMR 2010) MKL (Vedaldi et al. 2009) Impact Sensitivity occlusion trunc size aspect view part_vis
19 Summarizing Detector Performance Occlusion: high sensitivity, low potential impact Best, Average, Worst Case DPM (FGMR 2010) MKL (Vedaldi et al. 2009) occlusion trunc size aspect view part_vis
20 Summarizing Detector Performance Best, Average, Worst Case MKL more sensitive to size DPM (FGMR 2010) MKL (Vedaldi et al. 2009) occlusion trunc size aspect view part_vis
21 Conclusions Most errors are reasonable LocalizaIon error and confusion with similar objects MisdetecIon of occluded or small objects Large improvements in specific areas (e.g., remove all background FPs or robustness to occlusion) has small impact in overall AP More specific analysis should be standard Our code and annota8ons are available online AutomaIc generaion of analysis summary from standard annotaions
22 More Information: detectionanalysis_eccv12.tar.gz or: q=eccv% Thank you!
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