├── LICENSE ├── README.md ├── docs ├── H1 │ ├── facebook_cover_photo_1.png │ ├── facebook_cover_photo_2.png │ ├── facebook_profile_image.png │ ├── favicon.png │ ├── instagram_profile_image.png │ ├── linkedin_banner_image_1.png │ ├── linkedin_banner_image_2.png │ ├── linkedin_profile_image.png │ ├── logo.png │ ├── logo_transparent.png │ ├── pinterest_board_photo.png │ ├── pinterest_profile_image.png │ ├── twitter_header_photo_1.png │ ├── twitter_header_photo_2.png │ ├── twitter_profile_image.png │ └── youtube_profile_image.png ├── css │ └── gmot40.css ├── download.html ├── img │ ├── GMOT.png │ ├── gmot40.png │ ├── gmot40_white.png │ └── logo.png ├── index.html ├── js │ └── scrolling-nav.js ├── logo.png ├── out_imageio.gif ├── result.html └── vendor │ ├── bootstrap │ ├── css │ │ └── bootstrap.min.css │ └── js │ │ └── bootstrap.bundle.min.js │ ├── datatable │ ├── jquery.dataTables.css │ └── jquery.dataTables.js │ ├── jquery-easing │ └── jquery.easing.min.js │ └── jquery │ └── jquery.min.js └── sample.png /LICENSE: -------------------------------------------------------------------------------- 1 | Attribution-NonCommercial-ShareAlike 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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For 434 | the avoidance of doubt, this paragraph does not form part of the 435 | public licenses. 436 | 437 | Creative Commons may be contacted at creativecommons.org. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GMOT-40 Benchmark 2 | [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa] 3 | 4 | This is the official development project for paper: [GMOT-40: A Benchmark for Generic Multiple Object Tracking](https://arxiv.org/abs/2011.11858). 5 | 6 | To get the dataset, including sequences and labels, please go to the [official website](https://spritea.github.io/GMOT40). 7 | 8 | 9 | 10 | ## Abstract 11 | Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years. However, existing studies dominantly request prior knowledge of the tracking target (eg, pedestrians), and hence may not generalize well to unseen categories. In contrast, Generic Multiple Object Tracking (GMOT), which requires little prior information about the target, is largely under-explored. In this paper, we make contributions to boost the study of GMOT in three aspects. First, we construct the first publicly available dense GMOT dataset, dubbed GMOT-40, which contains 40 carefully annotated sequences evenly distributed among 10 object categories. In addition, two tracking protocols are adopted to evaluate different characteristics of tracking algorithms. Second, by noting the lack of devoted tracking algorithms, we have designed a series of baseline GMOT algorithms. Third, we perform a thorough evaluations on GMOT-40, involving popular MOT algorithms (with necessary modifications) and the proposed baselines. 12 | 13 | ## Baseline 14 | Below are the results of baseline methods with one-shot GMOT protocol. We randomly 15 | sample the one target in the 1st frame for the protocol and repeat this procedure for 5 times. Then we report the mean and standard deviation of the results over these 5 experiments. 16 | 17 | | Methods | MOTA | MOTP | IDF1 | 18 | |-------|-------|-------| -------| 19 | | [MDP](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Xiang_Learning_to_Track_ICCV_2015_paper.pdf) | 19.92% ± 1.84% | 24.16% ± 0.27% | 31.84% ± 2.23% | 20 | | [DeepSORT](https://arxiv.org/pdf/1703.07402.pdf) | 14.98% ± 1.47% | 23.66% ± 0.53% | 25.38% ± 2.32% | 21 | | [IOU Tracker](http://elvera.nue.tu-berlin.de/typo3/files/1517Bochinski2017.pdf) | 12.36% ± 1.60% | 25.34% ± 0.36% | 20.90% ± 2.73% | 22 | | [FAMNet](https://openaccess.thecvf.com/content_ICCV_2019/papers/Chu_FAMNet_Joint_Learning_of_Feature_Affinity_and_Multi-Dimensional_Assignment_for_ICCV_2019_paper.pdf) | 17.60% ± 0.85% | 22.56% ± 0.23% | 27.76% ± 1.16% | 23 | 24 | ## Citation 25 | The proceeding version is shown below. You may check the [arXiv version](https://arxiv.org/abs/2011.11858) for updates. 26 | 27 | ``` 28 | @inproceedings{bai2021gmot, 29 | title={GMOT-40: A Benchmark for Generic Multiple Object Tracking}, 30 | author={Bai, Hexin and Cheng, Wensheng and Chu, Peng and Liu, Juehuan and Zhang, Kai and Ling, Haibin}, 31 | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 32 | pages={6719--6728}, 33 | year={2021} 34 | } 35 | ``` 36 | 37 | ## Notes 38 | Although we tried to make the annotation quality as high as possible, there is still room for improvement, due to personal bias, etc. We would continue to improve the annotation quality since the first release. 39 | 40 | If you find any inproper label/have better annotation ideas, please contact Wensheng Cheng by Email: wenscheng at cs.stonybrook.edu. 41 | 42 | Your effort would be greatly appreciated, and the person whose improvement is adopted would be added to a contribution list! 43 | 44 | ## License 45 | 46 | 47 | This work is licensed under a 48 | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. 49 | 50 | 51 | 52 | [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ 53 | 54 | [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg -------------------------------------------------------------------------------- /docs/H1/facebook_cover_photo_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Spritea/GMOT40/da548c6ee0fac8df8a292e0c04b6c1ab8d046a05/docs/H1/facebook_cover_photo_1.png -------------------------------------------------------------------------------- /docs/H1/facebook_cover_photo_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Spritea/GMOT40/da548c6ee0fac8df8a292e0c04b6c1ab8d046a05/docs/H1/facebook_cover_photo_2.png 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138 | .category { 139 | display: flex; 140 | flex-wrap: wrap; 141 | padding: 10px; 142 | } 143 | 144 | .cate_table { 145 | padding: 0px; 146 | margin: 0px; 147 | border: 0px; 148 | border-style: solid; 149 | 150 | } 151 | 152 | .im_row { 153 | margin: 0px; 154 | padding-top: 12px; 155 | padding-bottom: 12px; 156 | } 157 | 158 | .im_col { 159 | border-left: 0px; 160 | border-top: 0px; 161 | border-bottom: 0px; 162 | border-right: 0px; 163 | border-style: solid; 164 | padding: 0px; 165 | } 166 | 167 | .im_column { 168 | flex: 25%; 169 | max-width: 25%; 170 | padding: 0 4px; 171 | } 172 | .im_row [class*='col-'] { 173 | padding: 0px; 174 | } 175 | .im_row [class*='col-'] img { 176 | height: 50px; 177 | width: 89px; 178 | display: block; 179 | margin-left: auto; 180 | margin-right: auto; 181 | margin-top: 3px; 182 | margin-bottom: 3px; 183 | vertical-align: middle; 184 | } 185 | 186 | .p_link { 187 | margin-bottom: 3px; 188 | } 189 | 190 | .row + .row { 191 | border-top:0; 192 | } 193 | 194 | .im_desc{ 195 | margin-top: 10px; 196 | font-size: 16px; 197 | font-weight: normal; 198 | } 199 | 200 | .im_desc table th{ 201 | border-right: 30px solid transparent; 202 | padding: 6px; 203 | width: 160px; 204 | } 205 | .tracker_table td, 206 | .tracker_table th { 207 | padding-left: 6px; 208 | padding-right: 6px; 209 | } 210 | 211 | .tracker_table td { 212 | border-top:1px solid #c9ced2; 213 | } 214 | 215 | .table-striped>tbody>tr:nth-child(even)>td, 216 | .table-striped>tbody>tr:nth-child(even)>th { 217 | background-color:#e9ecef; 218 | } -------------------------------------------------------------------------------- /docs/download.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | GMOT-40: A Benchmark for Generic Multiple Object Tracking 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 54 | 55 |
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58 |
59 |

GMOT-40 Data

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61 |

62 | GMOT-40 data consists of several parts, image sequences, trajectory labels, and detection results. 63 |

64 |
    65 |
  • 66 | Image Sequences: images of all 40 sequences.
    67 | Download link: GitHub Release. 68 |     69 |
    • 70 |
  • 71 | Trajectory labels: trajectory labels of all 40 sequences.
    72 | Download link: GitHub Release. 73 | 74 |     75 |
    • 76 |
  • 77 | Detection results: the detection results generated by our baseline one-shot detector.
    78 | Download link: GitHub Release. 79 |
    Welcome to contact us if you have better thoughts for labels! 80 |     81 | 82 |
    • 83 |
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Label Format

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96 | A brief description for trajectory label and detection results is shown below, with the image's top-left point as the coordinate origin. 97 |

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    99 |
  • 100 | Trajectory labels: one line of gt.txt is: [0, 5, 623, 307, 156, 57, 1, -1, -1, -1], 101 | corresponding to:
    102 | [frame_id, identity_id, box_top_left_x, box_top_left_y, box_width, box_height, confidence].
    103 | The confidence value is always 1 for all trajectory labels, and the last three -1 are just for padding. 104 |     105 |
    • 106 |
  • 107 | Detection results: one line of the detection result is: [0, -1, 474, 373, 150, 76, 0.998632], 108 | corresponding to:
    109 | [frame_id, identity_id, box_top_left_x, box_top_left_y, box_width, box_height, confidence].
    110 | The identity_id value is always -1 for all detection results. 111 |     112 |
    • 113 |
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Data File Structure

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126 | The file structure of image sequences is shown below. 127 |

128 | 
  |-- GenericMOT_JPEG/
129 |             |-- airplane-0/
130 |             |  |-- img1/
131 |             |  |  |-- 000000.jpg
132 |             |  |  |   ......
133 |             |  |  |-- 000375.jpg
134 |             |   ......
135 |             |-- stock-3/
136 |             |  |-- img1/
137 |             |  |  |-- 000000.jpg
138 |             |  |  |   ......
139 |             |  |  |-- 000312.jpg
140 |

141 | The file structure of detection results is simple, which contains the detection results of our baseline one-shot detector for all sequences. 142 |

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76 |

About GMOT-40(Jì Mò-40)

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79 | GMOT-40 is the first public dense dataset for Generic Multiple Object Tracking 80 | (GMOT). 81 | It contains 40 carefully annotated sequences evenly distributed among 10 object categories. 82 | Beyond the data, a challenging protocal, one-shot GMOT, is adopted and a series of baseline 83 | algorithms is introduced. 84 | GMOT-40 is featured in 85 |

86 |
    87 |
  • 88 | Dense objects. Over 80 objects of the same class could appear in one frame. 89 |
    • 90 | 91 |
  • 92 | High-quality label. Manual annotation with careful inspection in each frame. 93 |
    • 94 |
  • 95 | Diversity in target. Large variability between classes and within the sequences of 96 | the same class. 97 |
    • 98 |
  • 99 | Real world challenges. Occlusion, target enter/exiting, motion blur, deformation and 100 | so on. 101 |
    • 102 |
  • 103 | Challenging protocol. One-shot GMOT protocol is adopted for evaluation. 104 |
    • 105 |
  • 106 | New baselines. A series of baseline methods for one-shot GMOT task is introduced. 107 | 108 |
    • 109 | 110 |
111 |

Please check out the benchmark details and download links at Download page, baseline results at Leaderboard page. 114 |

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News

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    127 |
  • 128 | 2021-07: Trajectory labels are available. All data of GMOT-40 is released now! 129 |
    • 130 |
  • 131 | 2021-04: Image sequences and one-shot detection results are available! 132 |
    • 133 |
  • 134 | 2021-03: The official website is launched now! 135 | 136 |
    • 137 |
  • 138 | 2021-03: GMOT-40 paper is accepted to CVPR 2021! 139 |
    • 140 |
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Reference

151 |
152 | 153 |

Please consider citing GMOT-40 if it is helpful for you :)

154 | 160 | 161 |

GMOT-40: A Benchmark for Generic Multiple Object Tracking
162 | H. Bai*, W. Cheng*, P. Chu*, K. Zhang, and H. Ling
163 | IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 164 |

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Contact

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176 | 177 |

We appreciate questions and suggestions to Wensheng Cheng(general things) at wenscheng@cs.stonybrook.edu or Haibin Ling(commercial use) at hling@cs.stonybrook.edu.

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GMOT-40 Evaluation Instruction

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66 |

67 | Please check and follow instructions below for fair usage. 68 |

    69 |
  • 70 | Evaluation on GMOT-40: to compare with the baseline methods, please take the whole GMOT-40 dataset as the test set for consistency.
    71 | You may split the whole set to training/test set if needed for some reason.
    72 |     73 |
    • 74 |
  • 75 | One-shot detector: you are supposed to use the baseline one-shot detector provided by GMOT-40 for public track (public detector).
    76 | You may also use your own one-shot detector for private track (private detector). You would need to indicate this when submission. 77 |     78 | 79 |
    • 80 |
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Submission Guideline

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94 | Coming soon. 95 |

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Leaderboard

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107 | 110 | 111 |

112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 |
MethodsMOTA(%)IDF1(%)IDP(%)IDR(%)Rcll(%)Prcn(%)MTPTML↓FP↓FN↓IDs↓FM↓Date
MDP (Baseline)19.8031.3061.8021.0027.2080.20142621116117260186580177927482021-04-05
DeepSORT (Baseline)14.5024.4067.5014.9018.5084.107250913639000208818131522332021-04-05
IOU tracker (Baseline)11.8020.3064.6012.0015.4082.60563971491829921692175416682021-04-05
FAMNet (Baseline)18.0028.3054.8019.1026.8076.80166581119720741187730166018782021-04-05
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115 | } 116 | table.dataTable.display tbody tr.odd > .sorting_1, table.dataTable.order-column.stripe tbody tr.odd > .sorting_1 { 117 | background-color: #f1f1f1; 118 | } 119 | table.dataTable.display tbody tr.odd > .sorting_2, table.dataTable.order-column.stripe tbody tr.odd > .sorting_2 { 120 | background-color: #f3f3f3; 121 | } 122 | table.dataTable.display tbody tr.odd > .sorting_3, table.dataTable.order-column.stripe tbody tr.odd > .sorting_3 { 123 | background-color: whitesmoke; 124 | } 125 | table.dataTable.display tbody tr.odd.selected > .sorting_1, table.dataTable.order-column.stripe tbody tr.odd.selected > .sorting_1 { 126 | background-color: #a6b4cd; 127 | } 128 | table.dataTable.display tbody tr.odd.selected > .sorting_2, table.dataTable.order-column.stripe tbody tr.odd.selected > .sorting_2 { 129 | background-color: #a8b5cf; 130 | } 131 | table.dataTable.display tbody tr.odd.selected > .sorting_3, table.dataTable.order-column.stripe tbody tr.odd.selected > .sorting_3 { 132 | background-color: #a9b7d1; 133 | } 134 | table.dataTable.display tbody tr.even > .sorting_1, table.dataTable.order-column.stripe tbody tr.even > .sorting_1 { 135 | background-color: #fafafa; 136 | } 137 | table.dataTable.display tbody tr.even > .sorting_2, table.dataTable.order-column.stripe tbody tr.even > .sorting_2 { 138 | background-color: #fcfcfc; 139 | } 140 | table.dataTable.display tbody tr.even > .sorting_3, table.dataTable.order-column.stripe tbody tr.even > .sorting_3 { 141 | background-color: #fefefe; 142 | } 143 | table.dataTable.display tbody tr.even.selected > .sorting_1, table.dataTable.order-column.stripe tbody tr.even.selected > .sorting_1 { 144 | background-color: #acbad5; 145 | } 146 | table.dataTable.display tbody tr.even.selected > .sorting_2, table.dataTable.order-column.stripe tbody tr.even.selected > .sorting_2 { 147 | background-color: #aebcd6; 148 | } 149 | table.dataTable.display tbody tr.even.selected > .sorting_3, table.dataTable.order-column.stripe tbody tr.even.selected > .sorting_3 { 150 | background-color: #afbdd8; 151 | } 152 | table.dataTable.display tbody tr:hover > .sorting_1, table.dataTable.order-column.hover tbody tr:hover > .sorting_1 { 153 | background-color: #eaeaea; 154 | } 155 | table.dataTable.display tbody tr:hover > .sorting_2, table.dataTable.order-column.hover tbody tr:hover > .sorting_2 { 156 | background-color: #ececec; 157 | } 158 | table.dataTable.display tbody tr:hover > .sorting_3, table.dataTable.order-column.hover tbody tr:hover > .sorting_3 { 159 | background-color: #efefef; 160 | } 161 | table.dataTable.display tbody tr:hover.selected > .sorting_1, table.dataTable.order-column.hover tbody tr:hover.selected > .sorting_1 { 162 | background-color: #a2aec7; 163 | } 164 | table.dataTable.display tbody tr:hover.selected > .sorting_2, table.dataTable.order-column.hover tbody tr:hover.selected > .sorting_2 { 165 | background-color: #a3b0c9; 166 | } 167 | table.dataTable.display tbody tr:hover.selected > .sorting_3, table.dataTable.order-column.hover tbody tr:hover.selected > .sorting_3 { 168 | background-color: #a5b2cb; 169 | } 170 | table.dataTable.no-footer { 171 | border-bottom: 1px solid #111; 172 | } 173 | table.dataTable.nowrap th, table.dataTable.nowrap td { 174 | white-space: nowrap; 175 | } 176 | table.dataTable.compact thead th, 177 | table.dataTable.compact thead td { 178 | padding: 4px 17px; 179 | } 180 | table.dataTable.compact tfoot th, 181 | table.dataTable.compact tfoot td { 182 | padding: 4px; 183 | } 184 | table.dataTable.compact tbody th, 185 | table.dataTable.compact tbody td { 186 | padding: 4px; 187 | } 188 | table.dataTable th.dt-left, 189 | table.dataTable td.dt-left { 190 | text-align: left; 191 | } 192 | table.dataTable th.dt-center, 193 | table.dataTable td.dt-center, 194 | table.dataTable td.dataTables_empty { 195 | text-align: center; 196 | } 197 | table.dataTable th.dt-right, 198 | table.dataTable td.dt-right { 199 | text-align: right; 200 | } 201 | table.dataTable th.dt-justify, 202 | table.dataTable td.dt-justify { 203 | text-align: justify; 204 | } 205 | table.dataTable th.dt-nowrap, 206 | table.dataTable td.dt-nowrap { 207 | white-space: nowrap; 208 | } 209 | table.dataTable thead th.dt-head-left, 210 | table.dataTable thead td.dt-head-left, 211 | table.dataTable tfoot th.dt-head-left, 212 | table.dataTable tfoot td.dt-head-left { 213 | text-align: left; 214 | } 215 | table.dataTable thead th.dt-head-center, 216 | table.dataTable thead td.dt-head-center, 217 | table.dataTable tfoot th.dt-head-center, 218 | table.dataTable tfoot td.dt-head-center { 219 | text-align: center; 220 | } 221 | table.dataTable thead th.dt-head-right, 222 | table.dataTable thead td.dt-head-right, 223 | table.dataTable tfoot th.dt-head-right, 224 | table.dataTable tfoot td.dt-head-right { 225 | text-align: right; 226 | } 227 | table.dataTable thead th.dt-head-justify, 228 | table.dataTable thead td.dt-head-justify, 229 | table.dataTable tfoot th.dt-head-justify, 230 | table.dataTable tfoot td.dt-head-justify { 231 | text-align: justify; 232 | } 233 | table.dataTable thead th.dt-head-nowrap, 234 | table.dataTable thead td.dt-head-nowrap, 235 | table.dataTable tfoot th.dt-head-nowrap, 236 | table.dataTable tfoot td.dt-head-nowrap { 237 | white-space: nowrap; 238 | } 239 | table.dataTable tbody th.dt-body-left, 240 | table.dataTable tbody td.dt-body-left { 241 | text-align: left; 242 | } 243 | table.dataTable tbody th.dt-body-center, 244 | table.dataTable tbody td.dt-body-center { 245 | text-align: center; 246 | } 247 | table.dataTable tbody th.dt-body-right, 248 | table.dataTable tbody td.dt-body-right { 249 | text-align: right; 250 | } 251 | table.dataTable tbody th.dt-body-justify, 252 | table.dataTable tbody td.dt-body-justify { 253 | text-align: justify; 254 | } 255 | table.dataTable tbody th.dt-body-nowrap, 256 | table.dataTable tbody td.dt-body-nowrap { 257 | white-space: nowrap; 258 | } 259 | 260 | table.dataTable, 261 | table.dataTable th, 262 | table.dataTable td { 263 | box-sizing: content-box; 264 | } 265 | 266 | /* 267 | * Control feature layout 268 | */ 269 | .dataTables_wrapper { 270 | position: relative; 271 | clear: both; 272 | *zoom: 1; 273 | zoom: 1; 274 | } 275 | .dataTables_wrapper .dataTables_length { 276 | float: left; 277 | } 278 | .dataTables_wrapper .dataTables_length select { 279 | border: 1px solid #aaa; 280 | border-radius: 3px; 281 | padding: 5px; 282 | background-color: transparent; 283 | padding: 4px; 284 | } 285 | .dataTables_wrapper .dataTables_filter { 286 | float: right; 287 | text-align: right; 288 | } 289 | .dataTables_wrapper .dataTables_filter input { 290 | border: 1px solid #aaa; 291 | border-radius: 3px; 292 | padding: 5px; 293 | background-color: transparent; 294 | margin-left: 3px; 295 | } 296 | .dataTables_wrapper .dataTables_info { 297 | clear: both; 298 | float: left; 299 | padding-top: 0.755em; 300 | } 301 | .dataTables_wrapper .dataTables_paginate { 302 | float: right; 303 | text-align: right; 304 | padding-top: 0.25em; 305 | } 306 | .dataTables_wrapper .dataTables_paginate .paginate_button { 307 | box-sizing: border-box; 308 | display: inline-block; 309 | min-width: 1.5em; 310 | padding: 0.5em 1em; 311 | margin-left: 2px; 312 | text-align: center; 313 | text-decoration: none !important; 314 | cursor: pointer; 315 | *cursor: hand; 316 | color: #333 !important; 317 | border: 1px solid transparent; 318 | border-radius: 2px; 319 | } 320 | .dataTables_wrapper .dataTables_paginate .paginate_button.current, .dataTables_wrapper .dataTables_paginate .paginate_button.current:hover { 321 | color: #333 !important; 322 | border: 1px solid #979797; 323 | background-color: white; 324 | background: -webkit-gradient(linear, left top, left bottom, color-stop(0%, white), color-stop(100%, #dcdcdc)); 325 | /* Chrome,Safari4+ */ 326 | background: -webkit-linear-gradient(top, white 0%, #dcdcdc 100%); 327 | /* Chrome10+,Safari5.1+ */ 328 | background: -moz-linear-gradient(top, white 0%, #dcdcdc 100%); 329 | /* FF3.6+ */ 330 | background: -ms-linear-gradient(top, white 0%, #dcdcdc 100%); 331 | /* IE10+ */ 332 | background: -o-linear-gradient(top, white 0%, #dcdcdc 100%); 333 | /* Opera 11.10+ */ 334 | background: linear-gradient(to bottom, white 0%, #dcdcdc 100%); 335 | /* W3C */ 336 | } 337 | .dataTables_wrapper .dataTables_paginate .paginate_button.disabled, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled:hover, .dataTables_wrapper .dataTables_paginate .paginate_button.disabled:active { 338 | cursor: default; 339 | color: #666 !important; 340 | border: 1px solid transparent; 341 | background: transparent; 342 | box-shadow: none; 343 | } 344 | .dataTables_wrapper .dataTables_paginate .paginate_button:hover { 345 | color: white !important; 346 | border: 1px solid #111; 347 | background-color: #585858; 348 | background: -webkit-gradient(linear, left top, left bottom, color-stop(0%, #585858), color-stop(100%, #111)); 349 | /* Chrome,Safari4+ */ 350 | background: -webkit-linear-gradient(top, #585858 0%, #111 100%); 351 | /* Chrome10+,Safari5.1+ */ 352 | background: -moz-linear-gradient(top, #585858 0%, #111 100%); 353 | /* FF3.6+ */ 354 | background: -ms-linear-gradient(top, #585858 0%, #111 100%); 355 | /* IE10+ */ 356 | background: -o-linear-gradient(top, #585858 0%, #111 100%); 357 | /* Opera 11.10+ */ 358 | background: linear-gradient(to bottom, #585858 0%, #111 100%); 359 | /* W3C */ 360 | } 361 | .dataTables_wrapper .dataTables_paginate .paginate_button:active { 362 | outline: none; 363 | background-color: #2b2b2b; 364 | background: -webkit-gradient(linear, left top, left bottom, color-stop(0%, #2b2b2b), color-stop(100%, #0c0c0c)); 365 | /* Chrome,Safari4+ */ 366 | background: -webkit-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%); 367 | /* Chrome10+,Safari5.1+ */ 368 | background: -moz-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%); 369 | /* FF3.6+ */ 370 | background: -ms-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%); 371 | /* IE10+ */ 372 | background: -o-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%); 373 | /* Opera 11.10+ */ 374 | background: linear-gradient(to bottom, #2b2b2b 0%, #0c0c0c 100%); 375 | /* W3C */ 376 | box-shadow: inset 0 0 3px #111; 377 | } 378 | .dataTables_wrapper .dataTables_paginate .ellipsis { 379 | padding: 0 1em; 380 | } 381 | .dataTables_wrapper .dataTables_processing { 382 | position: absolute; 383 | top: 50%; 384 | left: 50%; 385 | width: 100%; 386 | height: 40px; 387 | margin-left: -50%; 388 | margin-top: -25px; 389 | padding-top: 20px; 390 | text-align: center; 391 | font-size: 1.2em; 392 | background-color: white; 393 | background: -webkit-gradient(linear, left top, right top, color-stop(0%, rgba(255, 255, 255, 0)), color-stop(25%, rgba(255, 255, 255, 0.9)), color-stop(75%, rgba(255, 255, 255, 0.9)), color-stop(100%, rgba(255, 255, 255, 0))); 394 | background: -webkit-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); 395 | background: -moz-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); 396 | background: -ms-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); 397 | background: -o-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); 398 | background: linear-gradient(to right, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); 399 | } 400 | .dataTables_wrapper .dataTables_length, 401 | .dataTables_wrapper .dataTables_filter, 402 | .dataTables_wrapper .dataTables_info, 403 | .dataTables_wrapper .dataTables_processing, 404 | .dataTables_wrapper .dataTables_paginate { 405 | color: #333; 406 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