Dataset of Unconstrained Large Gathering Images for Person Identification and Tracking

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed in the premises of Al Nabvi mosque, Madinah, Saudi Arabia. This dataset consists of both raw and processed images reflecting a highly challenging and unconstraint environment. The methodology for the development of the dataset consists of four core phases, 1) Acquisition of videos, 2) Extraction of frames, 3) Localization of face regions, and 4) Cropping and resizing of detected face regions. The raw images in the dataset consist of a total of 4613 frames obtained from video sequences. The processed images in the dataset consist of the face regions of 250 persons which were extracted from raw data images to ensure the authenticity of the presented data. The dataset further consists of 8 images corresponding to each of the 250 subjects (persons) for a total of 2000 images. The dataset portrays a highly unconstrained and challenging environment, where human faces of varying sizes and pixel quality (resolution) can be observed. Since the face regions in video sequences are severely degraded due to various unavoidable factors, it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research purposes. We have also gathered and displayed records of the presence of subjects who appear in presented frames; in a temporal context. This can also be used as a temporal benchmark for tracking, finding persons, activity monitoring, and crowd counting in large crowd scenarios

本论文构建了一款大规模集会场景图像数据集，该数据集的图像源自沙特阿拉伯麦地那先知清真寺（Al Nabvi Mosque）场地内部署的24台公共监控摄像机录制的视频素材。该数据集包含原始图像与处理后图像两类，对应场景极具挑战性且处于无约束状态。数据集的构建流程包含四个核心阶段：1）视频采集，2）帧提取，3）人脸区域定位，4）对检测出的人脸区域进行裁剪与尺寸调整。数据集中的原始图像共计4613帧，均来自视频序列。数据集中的处理后图像则是从原始图像中提取的250名受试者的人脸区域，以保障所发布数据的真实性。此外，数据集还为这250名受试者各提供8张图像，总计2000张处理后图像。该数据集对应的场景极具无约束性与挑战性，其中包含尺寸与像素质量（分辨率）各异的人脸图像。由于视频序列中的人脸区域会因多种不可抗因素出现严重退化，该数据集可作为基准测试集，用于科研场景下人脸检测与识别算法的测试与评估。本研究还采集并呈现了时序维度下各帧中出现的受试者的存在记录，该时序记录集可作为时序基准数据集，用于大规模人群场景下的人员追踪、人员检索、活动监测以及人群计数等研究任务。