ChokePoint Dataset

The ChokePoint dataset is designed for experiments in person identification/verification under real-world surveillance conditions using existing technologies. An array of three cameras was placed above several portals (natural choke points in terms of pedestrian traffic) to capture subjects walking through each portal in a natural way. While a person is walking through a portal, a sequence of face images (ie. a face set) can be captured. Faces in such sets will have variations in terms of illumination conditions, pose, sharpness, as well as misalignment due to automatic face localisation/detection. Due to the three camera configuration, one of the cameras is likely to capture a face set where a subset of the faces is near-frontal. The dataset consists of 25 subjects (19 male and 6 female) in portal 1 and 29 subjects (23 male and 6 female) in portal 2. The recording of portal 1 and portal 2 are one month apart. The dataset has frame rate of 30 fps and the image resolution is 800X600 pixels. In total, the dataset consists of 48 video sequences and 64,204 face images. In all sequences, only one subject is presented in the image at a time. The first 100 frames of each sequence are for background modelling where no foreground objects were presented. Each sequence was named according to the recording conditions (eg. P2E_S1_C3) where P, S, and C stand for portal, sequence and camera, respectively. E and L indicate subjects either entering or leaving the portal. The numbers indicate the respective portal, sequence and camera label. For example, P2L_S1_C3 indicates that the recording was done in Portal 2, with people leaving the portal, and captured by camera 3 in the first recorded sequence. To pose a more challenging real-world surveillance problems, two seqeunces (P2E_S5 and P2L_S5) were recorded with crowded scenario. In additional to the aforementioned variations, the sequences were presented with continuous occlusion. This phenomenon presents challenges in identidy tracking and face verification. This dataset can be applied, but not limited, to the following research areas: person re-identification image set matching face quality measurement face clustering 3D face reconstruction pedestrian/face tracking background estimation and subtraction Please cite the following paper if you use the ChokePoint dataset in your work (papers, articles, reports, books, software, etc): Y. Wong, S. Chen, S. Mau, C. Sanderson, B.C. Lovell Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops, pages 81-88, 2011. http://doi.org/10.1109/CVPRW.2011.5981881

ChokePoint数据集（ChokePoint Dataset）专为基于现有技术、在真实监控场景下开展人员身份识别/验证实验而设计。研究人员在多个出入口（行人通行场景下的天然瓶颈节点）上方部署了三台相机组成的阵列，以自然捕捉途经各出入口的行人影像。当行人途经出入口时，可采集到一系列人脸图像（即人脸集合）。此类人脸集合中的图像会存在光照、姿态、清晰度的差异，同时因自动人脸定位/检测环节的误差，还会出现人脸对齐偏差问题。得益于三台相机的部署配置，其中一台相机大概率能采集到包含近正脸人脸子集的人脸集合。
该数据集包含两个出入口的采集数据：出入口1涵盖25名受试者（19名男性、6名女性），出入口2涵盖29名受试者（23名男性、6名女性）。出入口1与出入口2的采集工作间隔时长为一个月。数据集的帧率为30帧每秒（fps），图像分辨率为800×600像素。整体而言，该数据集共包含48个视频序列与64204张人脸图像。所有视频序列中，单帧图像内仅包含一名受试者。每个视频序列的前100帧用于背景建模，此阶段无前景目标出现。
每个视频序列的命名规则基于采集场景设定（例如P2E_S1_C3）：其中P、S、C分别代表出入口（portal）、序列（sequence）与相机（camera）；E与L分别表示行人正进入或离开该出入口；数字则依次对应出入口、序列与相机的编号。例如，P2L_S1_C3代表此次采集工作在出入口2进行，行人正离开该出入口，且由第3台相机在首个采集序列中完成拍摄。
为构建更具挑战性的真实监控场景问题，研究人员采集了两段包含拥挤场景的视频序列（P2E_S5与P2L_S5）。除上述各类差异外，此类序列还存在持续遮挡的情况，这给身份跟踪与人脸验证任务带来了挑战。
该数据集的适用研究方向包括但不限于：行人重识别（person re-identification）、图像集合匹配、人脸质量评估、人脸聚类、三维人脸重建（3D face reconstruction）、行人/人脸跟踪以及背景估计与背景减除。
若您在研究工作（包括论文、文章、报告、书籍、软件等）中使用ChokePoint数据集，请引用以下文献：Y. Wong、S. Chen、S. Mau、C. Sanderson、B.C. Lovell. 基于补丁的概率图像质量评估：人脸选取与改进的视频人脸识别方法 // 电气和电子工程师协会（Institute of Electrical and Electronics Engineers, IEEE）生物特征识别研讨会，计算机视觉与模式识别（CVPR）研讨会. 2011年：81-88. https://doi.org/10.1109/CVPRW.2011.5981881

搜集汇总

背景与挑战

背景概述

ChokePoint Dataset是一个面向监控场景下身份识别研究的数据集，包含多摄像头捕捉的不同光照和姿态的面部图像序列，总计64,204张图像。数据集特别设计了拥挤和遮挡场景，适用于人脸识别、行人追踪等多种研究领域。