├── LICENSE.md ├── README.md ├── config └── doc │ ├── capture_ratio.png │ ├── data_ratio.png │ ├── degree.png │ ├── dir_structure.png │ ├── example1.png │ ├── example2.png │ ├── example3.png │ ├── example4.png │ ├── general_example.png │ ├── gt_example.png │ ├── occlusion.png │ ├── pose_distribution.png │ ├── space_ratio.png │ ├── top_example.png │ ├── truncation.png │ └── weather_ratio.png └── visualize_data.py /LICENSE.md: -------------------------------------------------------------------------------- 1 | (c) 2022 NCSOFT Corporation & UVify Co., Ltd. All Rights Reserved. 2 | 3 | Attribution 4.0 International 4 | 5 | ======================================================================= 6 | 7 | Creative Commons Corporation ("Creative Commons") is not a law firm and 8 | does not provide legal services or legal advice. Distribution of 9 | Creative Commons public licenses does not create a lawyer-client or 10 | other relationship. 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For 394 | the avoidance of doubt, this paragraph does not form part of the 395 | public licenses. 396 | 397 | Creative Commons may be contacted at creativecommons.org. 398 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Human tracking dataset captured by drone. 3 | ## Introduction 4 | * We have captured 500 videos by drone in various environment for multi object tracking task. Images were extracted from 30 frame per second video at different rate for training and test purposes. All extracted images are annotated by human with various properties such as occlusion, re-identification, visibility, pose, image truncation. 5 | 6 | 7 |

8 | drawing 9 | drawing 10 |

11 | 12 | ## Label format 13 | * Figure below is the example of ground truth file. 14 | 15 |

16 | drawing 17 |

18 | 19 | | Position | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 20 | |----------|--------------|-----------------|-------------------|----------------|----------------|-----------|------------|----------|------------|-----------|-----------|------------| 21 | | | frame_number | person_id_index | tracking_id_index | box_top_left_x | box_top_left_y | box_width | box_height | is_valid | pose_class | occlusion | truncated | visibility | 22 | 23 | ## Description of properties 24 | 25 | | Key | Value type | Description | 26 | |----------------|-------------|---------------------------------------------------------------------------------------------------| 27 | | frame_number | INT | Image frame number | 28 | | person_id | INT | Unique number assinged to person in the sequence (i.e., same person in the sequence will have same number regardeless of appear/disapper) | 29 | | tracking_id | INT | Unique number assigned to track of person (i.e., when same person disapeared and appeared again in the sequence, this number will be different) | 30 | | box_top_left_x | INT | X coordinate of top left corner | 31 | | box_top_left_y | INT | Y coordinate of top left corner | 32 | | box_width | INT | box width | 33 | | box_height | INT | box height | 34 | | is_valid | INT (0/1) | 0 : invalud instance (e.g., human in the picture), 1 : valid instance | 35 | | pose_class | INT (0/1/2) | 0 : General pose, 1 : Sitting, 2 : Waving hands | 36 | | occlusion | INT (0/1) | 0 : occlusion , 1 : no occlusion | 37 | | truncated | INT (0/1) | 0 : truncated , 1 : no truncated | 38 | | visibility | FLOAT | area of actual visible area in pixel / area of object size in the image predicted by human labeler | 39 | 40 | 41 | ## Download 42 | 43 | - [Download](https://uvify.sharepoint.com/:f:/s/research/EtBbHTxuaLJMlQM0aIkIF9UBnN2hd1nQ4ZLlkRIRTX4z5g?e=NZZeeZ) 44 | - Train images were extracted from video taken from drone at 1 frame per second. 45 | - This is for model training. 46 | - Test images were extracted from video taken from drone at 3 frame per second. 47 | - This is for evaluating the trained model. 48 | 49 | ## Directory Structure 50 | 
 51 |  [root path]/
 52 |  └──[test or train]/                  
 53 |     └──[seqence_(sequence number)]/
 54 |        ├──(sequence_number).txt     <--- ground truth
 55 |        └──[images]/
 56 |           └──(frame_number).png     <--- image
 57 |
58 | 59 | 60 | 61 | 62 | ## Visualizer 63 | 64 | ``` ruby 65 | python3 visualize_data.py --dir=[root_dir_path] --type=[train or test] --seq=[sequence number( 450 <= test < 500 , 0 <= train < 450)] 66 | ``` 67 | 68 | 
 69 | key 'q' 'esc' : exit program
 70 | key 'n' '->'  : next frame
 71 | key 'p' '<-'  : prev frame
 72 |
73 | 74 | ## Data Distribution 75 | 76 | ### Dataset 77 | | | Number of images | Description | 78 | |-----|------------------|------------------------------------------| 79 | |train| 13,500 | Dataset used during the learning process | 80 | |test | 4,500 | Dataset used only to assess performance | 81 | |total| 18,000 | | 82 | 83 | 84 | 85 | ### Drone camera angle 86 | | | Number of images | Description | 87 | |--------|------------------|----------------------------------------------------------------------------------------| 88 | |general | 14,370 | Dataset taken when the angle of the drone camera to the ground is between 10~60 degrees| 89 | |top | 3,630 | Dataset taken when the angle of the drone camera to the ground is around 90 degrees | 90 | |total | 18,000 | | 91 | 92 | 93 | ### Weather 94 | | | Number of images | Description | 95 | |--------|------------------|---------------------------------------------| 96 | |sunny | 10,320 | Dataset taken on a sunny day | 97 | |cloudy | 7,680 | Dataset taken on a rainy or cloudy day | 98 | |total | 18,000 | | 99 | 100 | ### Filming location 101 | | | Number of images | Description | 102 | |----------------|------------------|-------------------------------------------------------------------------------------| 103 | |general | 9,000 | Dataset taken from common roads such as side walk and asphalt | 104 | |green | 6,900 | Dataset taken in natural environments | 105 | |play ground | 2,100 | Dataset taken from outdoor sports venues such as tennis courts and basketball courts| 106 | |total | 18,000 | | 107 | 108 | 109 | 110 | 116 | 117 | ## Class Distribution 118 | 119 | ### Human pose class 120 | | | Number of objects| Description | 121 | |-----------|------------------|---------------------------------------------------------------------------------------| 122 | |general | 134,328 | Objects(people) with comman posture, such as walking or standing | 123 | |sitting | 9,598 | Objects(people) with sitting posutre | 124 | |waving hand| 542 | Object(people) waving hands | 125 | |total | 144,468 | | 126 | 127 | 128 | ### Occlusion 129 | | | Number of objects| Description | 130 | |------------|------------------|--------------------------------------------------------------------------------------| 131 | |occluded | 49,258 | Objects that are obscured by other objects within the image | 132 | |not occluded| 95,210 | Objects that appear completely without occlusion within the image | 133 | |total | 144,468 | | 134 | 135 | 136 | 137 | ### Truncation 138 | | | Number of objects| Description | 139 | |-------------|------------------|--------------------------------------------------------------------------------------| 140 | |truncated | 10,508 | Objects whose part is out of the image | 141 | |not truncated| 133,960 | Objects that appear completely without truncation within the image | 142 | |total | 144,468 | | 143 | 144 | 145 | 150 | 151 | 152 | ## Data Example 153 | * Images below are example images from dataset. 154 |

155 | drawing 156 | drawing 157 |

158 | 159 |

160 | drawing 161 | drawing 162 |

163 | 164 | ## Acknowledgement 165 | 166 | 이 데이터는 2021년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구의 결과물임 (No.171125972, 인명 구조용 드론을 위한 영상/음성 인지 기술 개발) 167 | 168 | This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.171125972, Audio-Visual Perception for Autonomous Rescue Drones) -------------------------------------------------------------------------------- /config/doc/capture_ratio.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uvify-public/human_tracking_dataset/eb3af0cfe49de018a0c4736581daadd8eb860883/config/doc/capture_ratio.png -------------------------------------------------------------------------------- /config/doc/data_ratio.png: -------------------------------------------------------------------------------- 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3 | from os import truncate 4 | 5 | import cv2 as cv 6 | import re 7 | import random 8 | import argparse 9 | 10 | from pandas.core import frame 11 | 12 | parser = argparse.ArgumentParser() 13 | parser.add_argument('--dir' , help='root directroy path') 14 | parser.add_argument('--type', help='data type (train < 450) , (test >= 450)') 15 | parser.add_argument('--seq' , help='sequence number') 16 | 17 | 18 | args = parser.parse_args() 19 | seq_path = args.dir + "/" + args.type + "/sequence_" + ("%05d" % int(args.seq)) 20 | 21 | txt_files = glob.glob(seq_path + '/**/*.txt', recursive=True) 22 | img_files = glob.glob(seq_path + '/**/*.png', recursive=True) 23 | 24 | img_files = sorted(img_files) 25 | id_color_map = dict() 26 | 27 | def get_pick_color(model): 28 | model_id = model 29 | r = int(model_id/(256**3)) 30 | g = int(model_id%(256**3)/(256**2)) 31 | b = int(model_id%(256**2)/256) 32 | a = int(model_id%256) 33 | return (r, g, b, a) 34 | 35 | 36 | cv.namedWindow("win") 37 | 38 | f = open(txt_files[0] , 'r') 39 | lines = f.readlines() 40 | 41 | regex = re.compile(r'\d+.png') 42 | 43 | i = 0 44 | while True: 45 | 46 | mat = cv.imread(img_files[i]) 47 | 48 | path_tokens = img_files[i].split(sep="/") 49 | 50 | sub_string = '/'.join(path_tokens[-5:-1]) 51 | 52 | img_file_name = path_tokens[-1] 53 | frame_number = int(re.search(r"(\d+).png",img_file_name).group(1)) 54 | 55 | for line in lines: 56 | line = line.strip() 57 | line_tokens = line.split(',') 58 | if(frame_number == int(line_tokens[0])): 59 | box_top_left_x = int(line_tokens[3]) 60 | box_top_left_y = int(line_tokens[4]) 61 | box_width = int(line_tokens[5]) 62 | box_hegiht = int(line_tokens[6]) 63 | 64 | 65 | person_id = "person id: " + line_tokens[1] 66 | tracking_id = "tracking id: " + line_tokens[2] 67 | is_valid = "is_valid: " + line_tokens[7] 68 | pose_class = "pose_class: " + line_tokens[8] 69 | occlusion = "occlusion: " + line_tokens[9] 70 | truncated = "truncated: "+ line_tokens[10] 71 | visibiltiy = "visibility: "+ line_tokens[11] 72 | 73 | try: 74 | color = id_color_map[line_tokens[1]] 75 | except KeyError: 76 | color = (random.randint(0,255), random.randint(0,255), random.randint(0,255)) 77 | id_color_map[line_tokens[1]] = color 78 | 79 | cv.rectangle(mat, (box_top_left_x, box_top_left_y), (box_top_left_x+box_width, box_top_left_y+box_hegiht), color, 3) 80 | cv.putText(mat, person_id, (box_top_left_x+10 + box_width, box_top_left_y+ 10) ,cv.FONT_HERSHEY_COMPLEX, 0.4, color, 1, cv.LINE_AA) 81 | cv.putText(mat, tracking_id, (box_top_left_x+10 + box_width, box_top_left_y + 25) ,cv.FONT_HERSHEY_COMPLEX, 0.4, color, 1, cv.LINE_AA) 82 | cv.putText(mat, is_valid, (box_top_left_x+10 + box_width, box_top_left_y+ 40) ,cv.FONT_HERSHEY_COMPLEX, 0.4, color, 1, cv.LINE_AA) 83 | cv.putText(mat, pose_class, (box_top_left_x+10 + box_width, box_top_left_y + 55) ,cv.FONT_HERSHEY_COMPLEX, 0.4, color, 1, cv.LINE_AA) 84 | cv.putText(mat, occlusion, (box_top_left_x+10 + box_width, box_top_left_y + 70) ,cv.FONT_HERSHEY_COMPLEX, 0.4, color, 1, cv.LINE_AA) 85 | cv.putText(mat, truncated, (box_top_left_x+10 + box_width, box_top_left_y + 85) ,cv.FONT_HERSHEY_COMPLEX, 0.4, color, 1, cv.LINE_AA) 86 | cv.putText(mat, visibiltiy, (box_top_left_x+10 + box_width, box_top_left_y + 100) ,cv.FONT_HERSHEY_COMPLEX, 0.4, color, 1, cv.LINE_AA) 87 | 88 | 89 | cv.putText(mat, line_tokens[0] ,(30,30), cv.FONT_HERSHEY_COMPLEX, 1.0 , (255,255,255), 1, cv.LINE_AA) 90 | 91 | 92 | 93 | 94 | cv.imshow("win", mat) 95 | key = cv.waitKey(0) 96 | if(key == 27 or key == 113): 97 | exit() 98 | 99 | elif(key==112 or key == 81): 100 | if(i >1 ): 101 | i = i-1 102 | 103 | else: 104 | i = i+1 105 | 106 | 107 | --------------------------------------------------------------------------------