The PASCAL Visual Object Classes Challenge 2010 (VOC2010)
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1 The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Part 4 Action Classification Taster Challenge Mark Everingham Luc Van Gool Ivan Laptev Chris Williams John Winn Andrew Zisserman
2 Motivation I: Actions describe many images Brushing hair Thinking Brushing teeth Playing saxophone Eating, Smiling Taking photo Playing cello Brushing shoes Driving car Typing on a laptop Playing piano Reading, Smiling
3 Motivation II: Actions in PASCAL VOC2009 Riding horse Riding horse Riding horse Riding motorbike Cycling Cycling Cycling Cycling Riding motorbike Riding motorbike Opening a bottle Taking a photograph Playing saxophone Cooking
4 Action Classification Taster Challenge Given the bounding box of a person, predict whether they are performing a given action Playing Instrument? Reading? Encourage research on still-image activity recognition: more detailed understanding of image
5 Nine Action Classes Phoning Playing Instrument Reading Riding Bike Riding Horse Running Taking Photo Using Computer Walking
6 Dataset Statistics Images collected from flickr using action queries Disjoint to main challenge dataset Training Testing Images Objects training objects per class Only subset of people are annotated (bounding box + action) All people in dataset are labelled with exactly one action class In future actions will not be mutually exclusive (or complete?)
7 Methods Comp9 (Train on VOC data): 11 Methods, 8 Groups Image classification within bounding box SVM, bag of words/spatial pyramid Multiple features: SIFT, PHOG, color SIFT, etc. Context (image, bounding box, neighbouring region) Classification of multiple figure-ground segmentations Combined image classification and part-based detection Comp10 (Train on own data): 1 Method Poselets, object context
8 AP by Class/Method Comp9 results phoning playing instrument reading riding bike riding horse running taking photo using computer walking BONN_ACTION CVC_BASE CVC_SEL INRIA_SPM_HT NUDT_SVM_WHGO_SIFT_CENTRIST_LLM SURREY_MK_KDA UCLEAR_SVM_DOSP_MULTFEATS UMCO_DHOG_KSVM WILLOW_A_SVMSIFT_1-A_LSVM Comp10 results WILLOW_LSVM WILLOW_SVMSIFT phoning playing instrument reading riding bike riding horse (1st, 2nd, 3rd place) running taking photo using computer walking BERKELEY_POSELETS_ACTION
9 Precision/Recall Riding Horse All results UCLEAR_SVM_DOSP_MULTFEATS (89.7) SURREY_MK_KDA (89.3) INRIA_SPM_HT (88.4) CVC_SEL (85.5) CVC_BASE (83.6) NUDT_SVM_WHGO_SIFT_CENTRIST_LLM (81.0) WILLOW_A_SVMSIFT_1-A_LSVM (77.1) WILLOW_SVMSIFT (76.7) BONN_ACTION (69.1) UMCO_DHOG_KSVM (68.8) WILLOW_LSVM (62.2) precision recall
10 Precision/Recall Taking Photo All results UMCO_DHOG_KSVM (34.1) SURREY_MK_KDA (32.8) UCLEAR_SVM_DOSP_MULTFEATS (32.5) BONN_ACTION (32.4) INRIA_SPM_HT (30.4) WILLOW_SVMSIFT (26.0) CVC_BASE (25.4) CVC_SEL (24.9) NUDT_SVM_WHGO_SIFT_CENTRIST_LLM (24.9) WILLOW_A_SVMSIFT_1-A_LSVM (24.3) WILLOW_LSVM (17.6) precision recall
11 AP by Class Max Median AP (%) ridinghorse running ridingbike walking usingcomputer playinginstrument phoning reading takingphoto Max AP: 89.7% (riding horse) % (taking photo)
12 Median AP by Method WILLOW_LSVM UMCO_DHOG_KSVM WILLOW_SVMSIFT AP (%) CVC_SEL INRIA_SPM_HT CVC_BASE UCLEAR_SVM_DOSP_MULTFEATS SURREY_MK_KDA NUDT_SVM_WHGO_SIFT_CENTRIST_LLM BONN_ACTION WILLOW_A_SVMSIFT_1-A_LSVM
13 True Positives : Riding Horse UCLEAR_SVM_DOSP_MULTFEATS SURREY_MK_KDA INRIA_SPM_HT
14 False Negatives : Riding Horse UCLEAR_SVM_DOSP_MULTFEATS SURREY_MK_KDA INRIA_SPM_HT
15 False Positives : Riding Horse UCLEAR_SVM_DOSP_MULTFEATS SURREY_MK_KDA INRIA_SPM_HT
16 True Positives : Walking CVC_SEL NUDT_SVM_WHGO_SIFT_CENTRIST_LLM UCLEAR_SVM_DOSP_MULTFEATS
17 False Negatives : Walking CVC_SEL NUDT_SVM_WHGO_SIFT_CENTRIST_LLM UCLEAR_SVM_DOSP_MULTFEATS
18 False Positives : Walking CVC_SEL NUDT_SVM_WHGO_SIFT_CENTRIST_LLM UCLEAR_SVM_DOSP_MULTFEATS
19 True Positives : Taking Photo UMCO_DHOG_KSVM SURREY_MK_KDA UCLEAR_SVM_DOSP_MULTFEATS
20 False Negatives : Taking Photo UMCO_DHOG_KSVM SURREY_MK_KDA UCLEAR_SVM_DOSP_MULTFEATS
21 False Positives : Taking Photo UMCO_DHOG_KSVM SURREY_MK_KDA UCLEAR_SVM_DOSP_MULTFEATS
22 Prizes Joint Winners CVC_SEL/CVC_BASE Nataliya Shapovalova, Wenjuan Gong, Fahad Shahbaz Khan, Josep M. Gonfaus, Marco Pedersoli, Andrew D. Bagdanov, Joost van de Weijer, Jordi González Computer Vision Center, Universitat Autonoma de Barcelona INRIA_SPM_HT Norberto Adrián Goussies, Arnau Ramisa, Cordelia Schmid INRIA LEAR NUDT_SVM_WHGO_SIFT_CENTRIST_LLM Li Zhou, Zongtan Zhou, Dewen Hu National University of Defence Technology SURREY_MK_KDA Piotr Koniusz, Muhammad Atif Tahir, Mark Barnard, Fei Yan, Krystian Mikolajczyk University of Surrey UCLEAR_SVM_DOSP_MULTFEATS Gaurav Sharma, Frederic Jurie, Cordelia Schmid University of Caen; INRIA LEAR UMCO_DHOG_KSVM Xutao Lv, Xiaoyu Wang, Xi Zhou, Tony X. Han University of Missouri - Columboa
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