Articulated Human Detection with Flexible Mixtures of Parts
We describe a method for articulated human detection and human pose estimation in static images based on a new representation of deformable part models. Rather than modeling articulation using a family of warped (rotated and foreshortened) templates, we use a mixture of small, nonoriented parts. We...
Saved in:
| Published in: | IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 12; pp. 2878 - 2890 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Los Alamitos, CA
IEEE
01.12.2013
IEEE Computer Society |
| Subjects: | |
| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | We describe a method for articulated human detection and human pose estimation in static images based on a new representation of deformable part models. Rather than modeling articulation using a family of warped (rotated and foreshortened) templates, we use a mixture of small, nonoriented parts. We describe a general, flexible mixture model that jointly captures spatial relations between part locations and co-occurrence relations between part mixtures, augmenting standard pictorial structure models that encode just spatial relations. Our models have several notable properties: 1) They efficiently model articulation by sharing computation across similar warps, 2) they efficiently model an exponentially large set of global mixtures through composition of local mixtures, and 3) they capture the dependency of global geometry on local appearance (parts look different at different locations). When relations are tree structured, our models can be efficiently optimized with dynamic programming. We learn all parameters, including local appearances, spatial relations, and co-occurrence relations (which encode local rigidity) with a structured SVM solver. Because our model is efficient enough to be used as a detector that searches over scales and image locations, we introduce novel criteria for evaluating pose estimation and human detection, both separately and jointly. We show that currently used evaluation criteria may conflate these two issues. Most previous approaches model limbs with rigid and articulated templates that are trained independently of each other, while we present an extensive diagnostic evaluation that suggests that flexible structure and joint training are crucial for strong performance. We present experimental results on standard benchmarks that suggest our approach is the state-of-the-art system for pose estimation, improving past work on the challenging Parse and Buffy datasets while being orders of magnitude faster. |
|---|---|
| AbstractList | We describe a method for articulated human detection and human pose estimation in static images based on a new representation of deformable part models. Rather than modeling articulation using a family of warped (rotated and foreshortened) templates, we use a mixture of small, nonoriented parts. We describe a general, flexible mixture model that jointly captures spatial relations between part locations and co-occurrence relations between part mixtures, augmenting standard pictorial structure models that encode just spatial relations. Our models have several notable properties: 1) They efficiently model articulation by sharing computation across similar warps, 2) they efficiently model an exponentially large set of global mixtures through composition of local mixtures, and 3) they capture the dependency of global geometry on local appearance (parts look different at different locations). When relations are tree structured, our models can be efficiently optimized with dynamic programming. We learn all parameters, including local appearances, spatial relations, and co-occurrence relations (which encode local rigidity) with a structured SVM solver. Because our model is efficient enough to be used as a detector that searches over scales and image locations, we introduce novel criteria for evaluating pose estimation and human detection, both separately and jointly. We show that currently used evaluation criteria may conflate these two issues. Most previous approaches model limbs with rigid and articulated templates that are trained independently of each other, while we present an extensive diagnostic evaluation that suggests that flexible structure and joint training are crucial for strong performance. We present experimental results on standard benchmarks that suggest our approach is the state-of-the-art system for pose estimation, improving past work on the challenging Parse and Buffy datasets while being orders of magnitude faster. We describe a method for articulated human detection and human pose estimation in static images based on a new representation of deformable part models. Rather than modeling articulation using a family of warped (rotated and foreshortened) templates, we use a mixture of small, nonoriented parts. We describe a general, flexible mixture model that jointly captures spatial relations between part locations and co-occurrence relations between part mixtures, augmenting standard pictorial structure models that encode just spatial relations. Our models have several notable properties: 1) They efficiently model articulation by sharing computation across similar warps, 2) they efficiently model an exponentially large set of global mixtures through composition of local mixtures, and 3) they capture the dependency of global geometry on local appearance (parts look different at different locations). When relations are tree structured, our models can be efficiently optimized with dynamic programming. We learn all parameters, including local appearances, spatial relations, and co-occurrence relations (which encode local rigidity) with a structured SVM solver. Because our model is efficient enough to be used as a detector that searches over scales and image locations, we introduce novel criteria for evaluating pose estimation and human detection, both separately and jointly. We show that currently used evaluation criteria may conflate these two issues. Most previous approaches model limbs with rigid and articulated templates that are trained independently of each other, while we present an extensive diagnostic evaluation that suggests that flexible structure and joint training are crucial for strong performance. We present experimental results on standard benchmarks that suggest our approach is the state-of-the-art system for pose estimation, improving past work on the challenging Parse and Buffy datasets while being orders of magnitude faster.We describe a method for articulated human detection and human pose estimation in static images based on a new representation of deformable part models. Rather than modeling articulation using a family of warped (rotated and foreshortened) templates, we use a mixture of small, nonoriented parts. We describe a general, flexible mixture model that jointly captures spatial relations between part locations and co-occurrence relations between part mixtures, augmenting standard pictorial structure models that encode just spatial relations. Our models have several notable properties: 1) They efficiently model articulation by sharing computation across similar warps, 2) they efficiently model an exponentially large set of global mixtures through composition of local mixtures, and 3) they capture the dependency of global geometry on local appearance (parts look different at different locations). When relations are tree structured, our models can be efficiently optimized with dynamic programming. We learn all parameters, including local appearances, spatial relations, and co-occurrence relations (which encode local rigidity) with a structured SVM solver. Because our model is efficient enough to be used as a detector that searches over scales and image locations, we introduce novel criteria for evaluating pose estimation and human detection, both separately and jointly. We show that currently used evaluation criteria may conflate these two issues. Most previous approaches model limbs with rigid and articulated templates that are trained independently of each other, while we present an extensive diagnostic evaluation that suggests that flexible structure and joint training are crucial for strong performance. We present experimental results on standard benchmarks that suggest our approach is the state-of-the-art system for pose estimation, improving past work on the challenging Parse and Buffy datasets while being orders of magnitude faster. |
| Author | Yi Yang Ramanan, Deva |
| Author_xml | – sequence: 1 surname: Yi Yang fullname: Yi Yang email: yyang8@ics.uci.edu organization: Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA – sequence: 2 givenname: Deva surname: Ramanan fullname: Ramanan, Deva email: dramanan@ics.uci.edu organization: Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28150229$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/24136428$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1kE1LAzEQhoNUtK1evQiyF8HL1nyb4KlUqwVFD3oOaXYWI9tdTbJY_71b2yoIngaG532ZeQaoVzc1IHRE8IgQrM-fHsf3sxHFhI6oJDuoT4nEuaaa9lAfE0lzpajaR4MYXzEmXGC2h_YpJ0xyqvrochySd21lExTZbbuwdXYFCVzyTZ19-PSSTStY-nkF2b1fpjZAzJoye7QhxQO0W9oqwuFmDtHz9PppcpvfPdzMJuO73DEuUq6FsMIRS7iThcQcdAlaK9FtmHIFgMNzKLli3blCCFKKubWWFQpLydwFZUN0tu59C817CzGZhY8OqsrW0LTREM4ZJwpf6A492aDtfAGFeQt-YcOn2T7cAacbwEZnqzLY2vn4yykiMKWrIr7mXGhiDFAa55NdWUnB-soQbFb-zbd_s_JvOv9dbPQntm3-N3C8DngA-IElU5hrxb4Ao16NhQ |
| CODEN | ITPIDJ |
| CitedBy_id | crossref_primary_10_3390_s17081913 crossref_primary_10_1109_TVT_2024_3426538 crossref_primary_10_1016_j_neunet_2022_03_041 crossref_primary_10_1049_iet_cvi_2017_0146 crossref_primary_10_1016_j_robot_2016_10_006 crossref_primary_10_1049_csy2_12045 crossref_primary_10_1145_3177852 crossref_primary_10_1049_iet_cvi_2017_0382 crossref_primary_10_1016_j_eswa_2020_113247 crossref_primary_10_1109_TPAMI_2019_2901875 crossref_primary_10_1007_s10489_023_04658_6 crossref_primary_10_3390_s18103202 crossref_primary_10_1007_s11548_025_03467_1 crossref_primary_10_3390_a18080533 crossref_primary_10_1007_s00530_017_0570_9 crossref_primary_10_1007_s11042_017_5133_8 crossref_primary_10_1016_j_media_2016_07_001 crossref_primary_10_1587_transinf_2015EDP7228 crossref_primary_10_1109_TPAMI_2016_2587642 crossref_primary_10_1016_j_patcog_2015_08_027 crossref_primary_10_1016_j_media_2022_102654 crossref_primary_10_1109_TPAMI_2016_2526002 crossref_primary_10_7717_peerj_cs_1152 crossref_primary_10_1109_ACCESS_2020_3010248 crossref_primary_10_1109_ACCESS_2024_3456436 crossref_primary_10_1016_j_sigpro_2014_09_014 crossref_primary_10_1088_1742_6596_1804_1_012178 crossref_primary_10_1109_TNNLS_2015_2411287 crossref_primary_10_1007_s11704_019_8266_2 crossref_primary_10_1109_TRO_2025_3577030 crossref_primary_10_1088_1757_899X_1275_1_012004 crossref_primary_10_1016_j_jbiomech_2017_01_028 crossref_primary_10_1016_j_patcog_2021_108046 crossref_primary_10_1016_j_jvcir_2015_06_013 crossref_primary_10_1109_TASE_2024_3389592 crossref_primary_10_1016_j_cmpb_2021_106189 crossref_primary_10_1109_ACCESS_2020_2973039 crossref_primary_10_1038_s41746_025_01929_z crossref_primary_10_1016_j_patcog_2020_107258 crossref_primary_10_1007_s11042_015_2608_3 crossref_primary_10_1109_ACCESS_2020_3026276 crossref_primary_10_1109_TII_2016_2605629 crossref_primary_10_1016_j_media_2018_03_012 crossref_primary_10_1016_j_cviu_2018_10_006 crossref_primary_10_3390_math13060893 crossref_primary_10_1007_s10462_024_10930_z crossref_primary_10_1007_s10994_025_06759_4 crossref_primary_10_1016_j_jvcir_2015_10_001 crossref_primary_10_1007_s10489_022_03549_6 crossref_primary_10_1049_el_2018_6478 crossref_primary_10_1609_aimag_v39i1_2776 crossref_primary_10_1016_j_eswa_2025_128922 crossref_primary_10_1016_j_jvcir_2020_102948 crossref_primary_10_1109_TCSVT_2022_3164663 crossref_primary_10_1007_s11263_024_02074_y crossref_primary_10_1109_TIP_2019_2955280 crossref_primary_10_1007_s11042_015_2611_8 crossref_primary_10_1016_j_cviu_2025_104297 crossref_primary_10_1016_j_neucom_2024_128049 crossref_primary_10_1016_j_ins_2016_06_016 crossref_primary_10_1007_s13198_022_01838_4 crossref_primary_10_1016_j_neucom_2016_11_038 crossref_primary_10_1016_j_patcog_2016_11_018 crossref_primary_10_1109_TCSVT_2025_3558125 crossref_primary_10_1007_s11263_016_0938_x crossref_primary_10_1016_j_neucom_2020_01_123 crossref_primary_10_1007_s10462_020_09904_8 crossref_primary_10_1109_ACCESS_2020_3011360 crossref_primary_10_1007_s00138_020_01089_y crossref_primary_10_1007_s00530_022_01019_0 crossref_primary_10_1007_s11263_015_0851_8 crossref_primary_10_1016_j_ins_2020_05_083 crossref_primary_10_1109_TPAMI_2016_2557783 crossref_primary_10_1016_j_cviu_2018_02_003 crossref_primary_10_1016_j_cviu_2019_04_011 crossref_primary_10_1109_ACCESS_2020_2977856 crossref_primary_10_1080_01691864_2024_2442721 crossref_primary_10_1016_j_actaastro_2025_02_015 crossref_primary_10_1016_j_inffus_2022_03_009 crossref_primary_10_1109_TPAMI_2020_3013620 crossref_primary_10_1145_3528223_3530106 crossref_primary_10_1007_s11042_018_5839_2 crossref_primary_10_1109_TPAMI_2023_3330935 crossref_primary_10_1177_1729881417714230 crossref_primary_10_1073_pnas_1802103115 crossref_primary_10_1109_ACCESS_2020_2966655 crossref_primary_10_3390_s16121966 crossref_primary_10_1007_s11263_021_01482_8 crossref_primary_10_1016_j_patcog_2019_03_010 crossref_primary_10_1109_TVCG_2024_3456141 crossref_primary_10_1038_s41598_024_62151_7 crossref_primary_10_1126_scirobotics_abe1315 crossref_primary_10_1016_j_cviu_2017_08_008 crossref_primary_10_1134_S0361768823080066 crossref_primary_10_1016_j_neucom_2014_07_069 crossref_primary_10_1109_TPAMI_2018_2865351 crossref_primary_10_1109_TAI_2023_3318575 crossref_primary_10_1016_j_sigpro_2014_07_031 crossref_primary_10_1007_s11042_018_5617_1 crossref_primary_10_1109_TCSVT_2021_3095489 crossref_primary_10_3390_jimaging9050104 crossref_primary_10_1109_TIP_2015_2473662 crossref_primary_10_1109_TMM_2016_2571629 crossref_primary_10_1016_j_cviu_2019_102897 crossref_primary_10_1038_s41598_025_91426_w crossref_primary_10_1007_s12652_020_02347_7 crossref_primary_10_1109_TIP_2019_2942686 crossref_primary_10_7554_eLife_63720 crossref_primary_10_3390_app13095402 crossref_primary_10_1007_s11263_018_1077_3 crossref_primary_10_1109_TITS_2019_2921325 crossref_primary_10_1109_ACCESS_2020_2995764 crossref_primary_10_1109_ACCESS_2024_3399222 crossref_primary_10_1109_ACCESS_2019_2902330 crossref_primary_10_1038_s41592_022_01443_0 crossref_primary_10_1007_s11042_018_6502_7 crossref_primary_10_3390_app12157909 crossref_primary_10_1016_j_cviu_2019_03_004 crossref_primary_10_1186_s42492_023_00148_1 crossref_primary_10_1145_3748717 crossref_primary_10_1016_j_cviu_2017_04_008 crossref_primary_10_17586_2226_1494_2025_25_2_273_285 crossref_primary_10_3724_SP_J_1089_2022_18878 crossref_primary_10_1016_j_envsoft_2024_106252 crossref_primary_10_1109_TIP_2018_2865666 crossref_primary_10_1038_s41598_022_16014_8 crossref_primary_10_1109_JBHI_2021_3062234 crossref_primary_10_3390_agronomy14123027 crossref_primary_10_3390_electronics14071307 crossref_primary_10_1109_TIP_2018_2872628 crossref_primary_10_1109_TPAMI_2014_2369050 crossref_primary_10_1007_s00138_023_01448_5 crossref_primary_10_1109_TIP_2022_3161081 crossref_primary_10_1007_s11263_016_0939_9 crossref_primary_10_1016_j_cviu_2021_103225 crossref_primary_10_1007_s00530_022_00980_0 crossref_primary_10_1016_j_patcog_2019_107074 crossref_primary_10_1109_TPAMI_2021_3087695 crossref_primary_10_1016_j_cviu_2018_01_001 crossref_primary_10_1111_exsy_12552 crossref_primary_10_3390_electronics14112107 crossref_primary_10_1007_s00138_015_0725_7 crossref_primary_10_1186_s13640_018_0255_0 crossref_primary_10_1016_j_inffus_2018_11_011 crossref_primary_10_1016_j_neucom_2024_128894 crossref_primary_10_1109_LSP_2014_2362553 crossref_primary_10_1145_3180420 crossref_primary_10_1109_TIP_2016_2639441 crossref_primary_10_1016_j_future_2022_12_002 crossref_primary_10_1016_j_compag_2025_110928 crossref_primary_10_1109_TCSVT_2017_2789224 crossref_primary_10_1016_j_neucom_2016_09_033 crossref_primary_10_1109_ACCESS_2019_2936709 crossref_primary_10_1016_j_neucom_2015_07_143 crossref_primary_10_1109_JSEN_2019_2929527 crossref_primary_10_1088_2057_1976_ad98a3 crossref_primary_10_3390_rs13132496 crossref_primary_10_1016_j_compbiomed_2025_110294 crossref_primary_10_1007_s11042_025_20704_6 crossref_primary_10_1109_JSEN_2020_3037121 crossref_primary_10_1016_j_neucom_2021_01_005 crossref_primary_10_1109_ACCESS_2020_3001473 crossref_primary_10_1109_TMM_2017_2762010 crossref_primary_10_1016_j_media_2016_05_003 crossref_primary_10_1109_TCSVT_2024_3435014 crossref_primary_10_1016_j_neucom_2025_130328 crossref_primary_10_4316_AECE_2015_04006 crossref_primary_10_1016_j_compag_2023_107736 crossref_primary_10_1080_01677063_2020_1804565 crossref_primary_10_1016_j_knosys_2017_06_001 crossref_primary_10_3389_fpls_2021_575751 crossref_primary_10_1038_s41598_025_96206_0 crossref_primary_10_1145_3524497 crossref_primary_10_1016_j_cviu_2016_12_002 crossref_primary_10_1061_JCCEE5_CPENG_5816 crossref_primary_10_1109_JSYST_2023_3270495 crossref_primary_10_1109_TPAMI_2020_2985395 crossref_primary_10_1155_2018_6271348 crossref_primary_10_1109_TPAMI_2021_3068236 crossref_primary_10_1109_ACCESS_2018_2808459 crossref_primary_10_1007_s11263_018_1081_7 crossref_primary_10_1109_ACCESS_2020_3010307 crossref_primary_10_1109_TAI_2022_3164065 crossref_primary_10_1016_j_cviu_2016_08_010 crossref_primary_10_1007_s11042_015_2819_7 crossref_primary_10_1109_TCSVT_2020_3042517 crossref_primary_10_1109_ACCESS_2020_3046845 crossref_primary_10_3233_JPD_223351 crossref_primary_10_1109_TCSVT_2017_2765242 crossref_primary_10_1007_s11119_023_10034_8 crossref_primary_10_1109_TPAMI_2019_2910523 crossref_primary_10_1109_TPAMI_2019_2929257 crossref_primary_10_1016_j_compag_2025_109961 crossref_primary_10_1016_j_autcon_2020_103308 crossref_primary_10_1049_iet_cvi_2016_0249 crossref_primary_10_3390_technologies10020047 crossref_primary_10_1007_s11760_019_01602_5 crossref_primary_10_1049_el_2017_4544 crossref_primary_10_1007_s11042_018_7119_6 crossref_primary_10_1145_2980179_2980235 crossref_primary_10_3390_rs12030465 crossref_primary_10_1109_TCSVT_2021_3059706 crossref_primary_10_1109_TIP_2022_3177959 crossref_primary_10_3390_s22051729 crossref_primary_10_1109_TPAMI_2016_2578328 crossref_primary_10_1016_j_patcog_2022_108652 crossref_primary_10_1109_ACCESS_2020_2980565 crossref_primary_10_1109_ACCESS_2020_2994283 crossref_primary_10_1109_TIP_2021_3077138 crossref_primary_10_1007_s11263_015_0869_y crossref_primary_10_1109_TPAMI_2019_2894422 crossref_primary_10_1109_JIOT_2025_3533556 crossref_primary_10_1109_LRA_2022_3151981 crossref_primary_10_1109_ACCESS_2016_2643439 crossref_primary_10_3390_s22052011 crossref_primary_10_3390_s25134028 crossref_primary_10_1016_j_jvcir_2021_103055 crossref_primary_10_1109_TCSVT_2020_3038145 crossref_primary_10_1145_3609235 crossref_primary_10_3390_s23094425 crossref_primary_10_1109_ACCESS_2019_2919154 crossref_primary_10_1016_j_patcog_2023_110048 crossref_primary_10_1109_TPAMI_2017_2731842 crossref_primary_10_1109_TCSVT_2023_3288370 crossref_primary_10_1049_ipr2_13111 crossref_primary_10_1109_TPAMI_2017_2724510 crossref_primary_10_1109_TIV_2018_2804170 crossref_primary_10_3390_s20061593 crossref_primary_10_1007_s41870_023_01497_z crossref_primary_10_3390_rs13173443 crossref_primary_10_1016_j_cviu_2016_11_002 crossref_primary_10_3390_en14030696 crossref_primary_10_1016_j_biosystemseng_2024_04_014 crossref_primary_10_1016_j_cag_2023_09_001 crossref_primary_10_1109_ACCESS_2019_2904117 crossref_primary_10_1007_s11263_016_0901_x crossref_primary_10_1109_TCSVT_2017_2707477 crossref_primary_10_3390_prosthesis6010002 crossref_primary_10_1016_j_cviu_2017_12_005 crossref_primary_10_1109_TIP_2021_3097836 crossref_primary_10_1109_TCSVT_2019_2952779 crossref_primary_10_1109_TPAMI_2021_3124736 crossref_primary_10_1109_TPAMI_2025_3552604 crossref_primary_10_3390_s21227640 crossref_primary_10_1016_j_patcog_2021_107863 crossref_primary_10_1016_j_cogsys_2017_08_001 crossref_primary_10_1109_JTEHM_2019_2892970 crossref_primary_10_1016_j_patcog_2016_12_025 crossref_primary_10_1109_TMM_2016_2556859 crossref_primary_10_3390_jimaging8110308 crossref_primary_10_1109_TPAMI_2016_2537807 crossref_primary_10_1007_s00138_022_01352_4 crossref_primary_10_3390_s22228819 crossref_primary_10_3390_sym17071098 crossref_primary_10_1007_s00138_020_01104_2 crossref_primary_10_1007_s12204_025_2815_7 crossref_primary_10_1109_ACCESS_2024_3376426 crossref_primary_10_1007_s10489_021_02718_3 crossref_primary_10_3390_app15137344 crossref_primary_10_1016_j_cviu_2022_103483 crossref_primary_10_1016_j_mechatronics_2022_102807 crossref_primary_10_1109_TSMC_2016_2639788 crossref_primary_10_1109_TIT_2019_2916805 crossref_primary_10_1111_cgf_13310 crossref_primary_10_1109_TPAMI_2015_2408349 crossref_primary_10_1016_j_neucom_2016_08_032 crossref_primary_10_1109_ACCESS_2020_2969994 crossref_primary_10_1109_TCSVT_2018_2879980 crossref_primary_10_1007_s12530_023_09508_x crossref_primary_10_1109_JPHOT_2024_3453116 crossref_primary_10_3390_s24175457 crossref_primary_10_1007_s11263_014_0767_8 crossref_primary_10_1016_j_jvcir_2022_103461 crossref_primary_10_1007_s00138_024_01608_1 |
| Cites_doi | 10.1007/s11263-009-0275-4 10.1109/T-C.1973.223602 10.1007/978-3-319-57021-1_9 10.1109/TPAMI.1980.6447699 10.1007/978-3-642-15561-1_17 10.1109/CVPR.2006.180 10.1145/1015330.1015341 10.1109/CVPR.2012.6248058 10.1145/1273496.1273508 10.1007/978-3-540-88690-7_53 10.1109/ICCV.2001.937589 10.1109/CVPR.2010.5540227 10.1109/CVPR.2007.383301 10.1109/ICCV.2009.5459192 10.1023/A:1011179004708 10.1109/CVPR.2010.5539879 10.1109/ICCV.2011.6126309 10.1016/0262-8856(83)90003-3 10.1007/3-540-47977-5_44 10.1109/CVPR.2011.5995741 10.1109/CVPR.2009.5206754 10.5244/C.23.3 10.1109/TPAMI.2009.167 10.5244/C.24.12 10.1007/978-3-642-15552-9_30 10.1109/CVPR.2011.5995318 10.1109/ICCV.2011.6126552 10.1007/3-540-47969-4_42 10.1109/CVPR.2008.4587468 10.1007/978-3-642-15558-1_23 10.1109/ICCV.2009.5459303 10.1109/CVPR.2005.177 10.1109/CVPR.2004.1315183 10.1006/cviu.1994.1006 10.1109/ICCVW.2009.5457673 10.1109/CVPR.2010.5539906 10.1109/CVPR.2006.315 10.1109/CVPR.2011.5995607 10.1007/s11263-012-0524-9 10.1109/CVPR.2004.1315182 10.7551/mitpress/7503.003.0146 10.1007/s11263-010-0375-1 10.1023/B:VISI.0000042934.15159.49 10.1109/CVPR.2010.5540182 10.1162/08997660360581958 10.1109/ICCV.2005.48 10.1007/978-0-85729-997-0_11 10.1109/CVPR.2007.383086 |
| ContentType | Journal Article |
| Copyright | 2015 INIST-CNRS |
| Copyright_xml | – notice: 2015 INIST-CNRS |
| DBID | 97E RIA RIE AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1109/TPAMI.2012.261 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Applied Sciences |
| EISSN | 2160-9292 1939-3539 |
| EndPage | 2890 |
| ExternalDocumentID | 24136428 28150229 10_1109_TPAMI_2012_261 6380498 |
| Genre | orig-research Journal Article |
| GroupedDBID | --- -DZ -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E 9M8 AAJGR AARMG AASAJ AAWTH ABAZT ABFSI ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT ADRHT AENEX AETEA AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P FA8 HZ~ H~9 IBMZZ ICLAB IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNI RNS RXW RZB TAE TN5 UHB VH1 XJT ~02 AAYXX CITATION AAYOK IQODW RIG CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c345t-955a5c1a14c6d604e9fe99851a138cdeec0bef4830165551f5baaa3d80663c723 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 552 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000326502200006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0162-8828 1939-3539 |
| IngestDate | Sun Nov 23 09:38:05 EST 2025 Mon Jul 21 06:02:35 EDT 2025 Wed Apr 02 07:37:54 EDT 2025 Sat Nov 29 08:08:20 EST 2025 Tue Nov 18 21:05:09 EST 2025 Tue Aug 26 16:41:28 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Keywords | Computer vision Motion estimation Pose estimation articulated shapes Spatial analysis Mixture theory Tree structure Modeling Posture Experimental result Object detection Dynamic programming deformable part models Localization Cooccurrence analysis Mechanical deformation |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c345t-955a5c1a14c6d604e9fe99851a138cdeec0bef4830165551f5baaa3d80663c723 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 24136428 |
| PQID | 1443418079 |
| PQPubID | 23479 |
| PageCount | 13 |
| ParticipantIDs | pubmed_primary_24136428 ieee_primary_6380498 pascalfrancis_primary_28150229 crossref_citationtrail_10_1109_TPAMI_2012_261 proquest_miscellaneous_1443418079 crossref_primary_10_1109_TPAMI_2012_261 |
| PublicationCentury | 2000 |
| PublicationDate | 2013-12-01 |
| PublicationDateYYYYMMDD | 2013-12-01 |
| PublicationDate_xml | – month: 12 year: 2013 text: 2013-12-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Los Alamitos, CA |
| PublicationPlace_xml | – name: Los Alamitos, CA – name: United States |
| PublicationTitle | IEEE transactions on pattern analysis and machine intelligence |
| PublicationTitleAbbrev | TPAMI |
| PublicationTitleAlternate | IEEE Trans Pattern Anal Mach Intell |
| PublicationYear | 2013 |
| Publisher | IEEE IEEE Computer Society |
| Publisher_xml | – name: IEEE – name: IEEE Computer Society |
| References | ref13 ref12 ref15 ref14 ref53 ref11 ref10 ref17 ref16 ref19 ref18 ref51 ref50 ref45 ref48 ref47 ref42 ref41 ref43 Fan (ref44) 2008; 9 ref49 ref7 Ferrari (ref8) 2013 ref9 ref4 ref3 ref5 Yang (ref6) 2013 ref40 Ramanan (ref46) 2012 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 Yang (ref52) 2013 ref28 ref27 ref29 |
| References_xml | – ident: ref49 doi: 10.1007/s11263-009-0275-4 – ident: ref2 doi: 10.1109/T-C.1973.223602 – ident: ref38 doi: 10.1007/978-3-319-57021-1_9 – ident: ref11 doi: 10.1109/TPAMI.1980.6447699 – ident: ref23 doi: 10.1007/978-3-642-15561-1_17 – ident: ref18 doi: 10.1109/CVPR.2006.180 – ident: ref43 doi: 10.1145/1015330.1015341 – ident: ref35 doi: 10.1109/CVPR.2012.6248058 – ident: ref45 doi: 10.1145/1273496.1273508 – ident: ref19 doi: 10.1007/978-3-540-88690-7_53 – ident: ref15 doi: 10.1109/ICCV.2001.937589 – volume: 9 start-page: 1871 year: 2008 ident: ref44 article-title: Liblinear: A Library for Large Linear Classification publication-title: J. Machine Learning Research – ident: ref20 doi: 10.1109/CVPR.2010.5540227 – ident: ref30 doi: 10.1109/CVPR.2007.383301 – year: 2013 ident: ref6 article-title: Flexible Mixtures of Parts for Articulated Pose Detection, Release 1.3 – ident: ref25 doi: 10.1109/ICCV.2009.5459192 – ident: ref17 doi: 10.1023/A:1011179004708 – ident: ref36 doi: 10.1109/CVPR.2010.5539879 – ident: ref37 doi: 10.1109/ICCV.2011.6126309 – ident: ref12 doi: 10.1016/0262-8856(83)90003-3 – ident: ref31 doi: 10.1007/3-540-47977-5_44 – year: 2013 ident: ref52 article-title: Flexible Mixtures of Parts for Articulated Pose Detection, Release 1.2 – ident: ref10 doi: 10.1109/CVPR.2011.5995741 – year: 2012 ident: ref46 article-title: Dual Coordinate Descent Solvers for Large Structured Prediction Problems – ident: ref27 doi: 10.1109/CVPR.2009.5206754 – ident: ref53 doi: 10.5244/C.23.3 – ident: ref4 doi: 10.1109/TPAMI.2009.167 – ident: ref50 doi: 10.5244/C.24.12 – ident: ref26 doi: 10.1007/978-3-642-15552-9_30 – year: 2013 ident: ref8 article-title: Buffy Stickmen v3.01: Annotated Data and Evaluation Routines for 2D Human Pose Estimation – ident: ref51 doi: 10.1109/CVPR.2011.5995318 – ident: ref42 doi: 10.1109/ICCV.2011.6126552 – ident: ref32 doi: 10.1007/3-540-47969-4_42 – ident: ref7 doi: 10.1109/CVPR.2008.4587468 – ident: ref21 doi: 10.1007/978-3-642-15558-1_23 – ident: ref3 doi: 10.1109/ICCV.2009.5459303 – ident: ref34 doi: 10.1109/CVPR.2005.177 – ident: ref16 doi: 10.1109/CVPR.2004.1315183 – ident: ref13 doi: 10.1006/cviu.1994.1006 – ident: ref33 doi: 10.1109/ICCVW.2009.5457673 – ident: ref5 doi: 10.1109/CVPR.2010.5539906 – ident: ref24 doi: 10.1109/CVPR.2006.315 – ident: ref41 doi: 10.1109/CVPR.2011.5995607 – ident: ref48 doi: 10.1007/s11263-012-0524-9 – ident: ref28 doi: 10.1109/CVPR.2004.1315182 – ident: ref9 doi: 10.7551/mitpress/7503.003.0146 – ident: ref40 doi: 10.1007/s11263-010-0375-1 – ident: ref1 doi: 10.1023/B:VISI.0000042934.15159.49 – ident: ref29 doi: 10.1109/CVPR.2010.5540182 – ident: ref47 doi: 10.1162/08997660360581958 – ident: ref22 doi: 10.1109/ICCV.2005.48 – ident: ref14 doi: 10.1007/978-0-85729-997-0_11 – ident: ref39 doi: 10.1109/CVPR.2007.383086 |
| SSID | ssj0014503 |
| Score | 2.6323993 |
| Snippet | We describe a method for articulated human detection and human pose estimation in static images based on a new representation of deformable part models. Rather... |
| SourceID | proquest pubmed pascalfrancis crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 2878 |
| SubjectTerms | Algorithms Applied sciences articulated shapes Artificial intelligence Computational modeling Computer science; control theory; systems Data processing. List processing. Character string processing Deformable models deformable part models Exact sciences and technology Human factors Humans Memory organisation. Data processing Models, Theoretical object detection Object segmentation Pattern recognition. Digital image processing. Computational geometry Pose estimation Reproducibility of Results Shape analysis Software |
| Title | Articulated Human Detection with Flexible Mixtures of Parts |
| URI | https://ieeexplore.ieee.org/document/6380498 https://www.ncbi.nlm.nih.gov/pubmed/24136428 https://www.proquest.com/docview/1443418079 |
| Volume | 35 |
| WOSCitedRecordID | wos000326502200006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2160-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014503 issn: 0162-8828 databaseCode: RIE dateStart: 19790101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9tAEB4F1AM9lFcLgRItElIvNTj2rr0WJwRE7QGUA5Vys_YxlpCQg3BS9ed3Zu24RIJDb5Y9K1szs95vdmbnAzirfGastD5yKrORtKaItFEZhSoKrVMVIQoTyCby-3s9mxXTAXzvz8IgYig-w3O-DLl8P3dL3iq7IF8hQKs3YCPPs_asVp8xkCqwIBOCoRlOYUTXoHEcFxcP06u7n1zFlZxTvMDtf-nPzbh7bS0K5CpcGmka0k7V0lq8jzvD-jPZ_r8v34FPHc4UV61j7MIA6z3YXnE4iG5K78HHVw0J9-EyyDOjF3oR9vfFDS5CtVYteMtWTLiBpn1Ccff4h5MPjZhXYkr-13yGX5Pbh-sfUUevELlUqkVUKGWUG5uxdJnPYolFhRR8KbqTaucRXWyxkjrlE08ErCpljTGp14xSXJ6kX2Czntd4CMLEFOk69JlVsZTeGqVSjJ00Js9tofMhRCtFl67rPc4UGE9liEHiogw2KtlGJdloCN96-ee268a7kvus7V6qU_QQRmt27J8nmhBwkhRDOF0ZtqQJxVkSU-N82VAsJGll13FOMgetxf-N7hzn6O23HsNWwmwZodrlK2wuXpZ4Ah_c78Vj8zIir53pUfDav3VG5vM |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS9xAEB_UCrYPWrUfV1vdQsEXo7lkN9ngk1QPRe-4hyv4FvZjAoLkirkr_vnO7OVShfrQt7CZJWFmlv3Nzuz8AH5UPjNWWh85ldlIWlNE2qiMQhWF1qmKEIUJZBP5aKRvb4vxChx1d2EQMRSf4TE_hly-n7o5H5WdkK8QoNWr8IaZs9rbWl3OQKrAg0wYhtY4BRJti8Z-XJxMxmfDK67jSo4pYuAGwDSdkfeL3SjQq3BxpGlIP9WC2OJ15Bl2oMHW__37e9hskaY4W7jGNqxgvQNbSxYH0S7qHXj3rCXhLpwGeeb0Qi_CCb84x1mo16oFH9qKAbfQtPcohnePnH5oxLQSY_LA5gP8GlxMfl5GLcFC5FKpZlGhlFGub_rSZT6LJRYVUvilaCTVziO62GIldcp3nghaVcoaY1KvGae4PEk_wlo9rfEzCBNTrOvQZ1bFUnprlEoxdtKYPLeFznsQLRVdurb7OJNg3JchComLMtioZBuVZKMeHHbyvxd9N16V3GVtd1Ktonuw_8KO3ftEEwZOkqIH35eGLWlJcZ7E1DidNxQNSdrbdZyTzKeFxf_Obh3ny7-_egAbl5PhTXlzNbreg7cJc2eE2pevsDZ7mOM3WHd_ZnfNw37w3SczhelU |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Articulated+Human+Detection+with+Flexible+Mixtures+of+Parts&rft.jtitle=IEEE+transactions+on+pattern+analysis+and+machine+intelligence&rft.au=Yi+Yang&rft.au=Ramanan%2C+Deva&rft.date=2013-12-01&rft.pub=IEEE&rft.issn=0162-8828&rft.volume=35&rft.issue=12&rft.spage=2878&rft.epage=2890&rft_id=info:doi/10.1109%2FTPAMI.2012.261&rft_id=info%3Apmid%2F24136428&rft.externalDocID=6380498 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0162-8828&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0162-8828&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0162-8828&client=summon |