Annotations for ConfLab A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild

This file contains the annotations for the ConfLab dataset, including actions (speaking status), pose, and F-formations. ------------------ ./actions/speaking_status: ./processed: the processed speaking status files, aggregated into a single data frame per segment. Skipped rows in the raw data (see https://josedvq.github.io/covfee/docs/output for details) have been imputed using the code at: https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/speaking_status The processed annotations consist of: ./speaking: The first row contains person IDs matching the sensor IDs, The rest of the row contains binary speaking status annotations at 60fps for the corresponding 2 min video segment (7200 frames). ./confidence: Same as above. These annotations reflect the continuous-valued rating of confidence of the annotators in their speaking annotation. To load these files with pandas: pd.read_csv(p, index_col=False) <br> ./raw-covfee.zip: the raw outputs from speaking status annotation for each of the eight annotated 2-min video segments. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. -------------------- ./pose: ./coco: the processed pose files in coco JSON format, aggregated into a single data frame per video segment. These files have been generated from the raw files using the code at: https://github.com/TUDelft-SPC-Lab/conflab-keypoints To load in Python: f = json.load(open('/path/to/cam2_vid3_seg1_coco.json')) The skeleton structure (limbs) is contained within each file in: f['categories'][0]['skeleton'] and keypoint names at: f['categories'][0]['keypoints'] ./raw-covfee.zip: the raw outputs from continuous pose annotation. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. --------------------- ./f_formations: seg 2: 14:00 onwards, for videos of the form x2xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). seg 3: for videos of the form x3xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). Note that camera 10 doesn't include meaningful subject information/body parts that are not already covered in camera 8. First column: time stamp Second column: "()" delineates groups, "&lt;&gt;" delineates subjects, cam X indicates the best camera view for which a particular group exists. <br> phone.csv: time stamp (pertaining to seg3), corresponding group, ID of person using the phone

本文件包含ConfLab数据集（ConfLab Dataset）的标注信息，涵盖行为（说话状态）、姿态（pose）以及F型群体结构（F-formations）。
------------------ ./actions/speaking_status：
./processed：经处理的说话状态文件，已按每个视频片段整合为单一数据框。原始数据中被跳过的行（详情请参阅https://josedvq.github.io/covfee/docs/output）已通过以下路径的代码完成补全：https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/speaking_status。经处理的标注信息包含：
./speaking：首行为与传感器ID匹配的人员ID，其余行对应2分钟视频片段（共7200帧）以60帧/秒速率标注的二值化说话状态。
./confidence：格式与上述一致。该标注反映标注人员对其说话状态标注结果的置信度连续取值评分。可通过pandas加载此类文件：pd.read_csv(p, index_col=False)
./raw-covfee.zip：8个经标注的2分钟视频片段各自的原始说话状态标注输出结果，由covfee标注工具（covfee annotation tool）生成，标注速率为60帧/秒。
-------------------- ./pose：
./coco：采用COCO JSON格式的经处理姿态文件，已按每个视频片段整合为单一数据框。此类文件通过以下路径的代码由原始文件生成：https://github.com/TUDelft-SPC-Lab/conflab-keypoints。可通过以下Python代码加载：f = json.load(open('/path/to/cam2_vid3_seg1_coco.json'))。骨骼结构（肢体）存储于每个文件的f['categories'][0]['skeleton']字段中，关键点名称则存储于f['categories'][0]['keypoints']字段。
./raw-covfee.zip：连续姿态标注的原始输出结果，由covfee标注工具（covfee annotation tool）生成，标注速率为60帧/秒。
--------------------- ./f_formations：
片段2：对应/video/raw/路径下格式为x2xxx.MP4的视频，适用摄像机为2、4、6、8、10，时间范围为14:00及以后。
片段3：对应/video/raw/路径下格式为x3xxx.MP4的视频，适用摄像机为2、4、6、8、10。需注意，摄像机10未提供摄像机8未覆盖的有效主体信息或肢体部位。
第一列为时间戳；第二列中，"()"用于界定群体，"<>"用于界定主体，cam X表示某一群体对应的最佳拍摄视角摄像机。
phone.csv：包含片段3的时间戳、对应群体以及使用手机的人员ID。