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<br> <strong>Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition </strong><br> <em>IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops</em>, pages 81-88, 2011.<br> http://doi.org/10.1109/CVPRW.2011.5981881ChokePoint数据集（ChokePoint Dataset）专为依托现有技术，在真实监控场景下开展人员识别与验证实验而构建。我们在多个出入口（portal，行人通行的天然瓶颈位置）上方部署了三台相机组成的阵列，以自然捕捉通过各出入口的行人。当行人通过出入口（portal）时，系统可捕捉到一系列人脸图像，即人脸集合（face set）。此类集合中的人脸会存在光照、姿态、清晰度的差异，同时还会因自动人脸定位/检测环节出现对齐偏差。由于采用三台相机的部署方案，其中至少有一台相机可捕捉到包含近正面人脸子集的人脸集合（face set）。 该数据集包含25名受试者（19名男性、6名女性）的出入口1数据，以及29名受试者（23名男性、6名女性）的出入口2数据。出入口1与出入口2的录制时间间隔为一个月。该数据集的帧率为30 fps（frames per second，每秒帧数），图像分辨率为800×600像素。数据集总计包含48个视频序列与64204张人脸图像。所有序列中，单张图像内仅出现一名受试者。每个序列的前100帧用于背景建模，此时无前景目标出现。 每个序列均按照录制条件命名（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分别表示受试者正进入或离开出入口（portal）。数字则分别对应出入口、序列与相机的编号。例如，P2L_S1_C3表示该录制于出入口2进行，受试者正离开该出入口，且由第3台相机在首个录制序列中完成采集。 为构建更具挑战性的真实监控场景问题，我们录制了两个包含拥挤场景的序列（P2E_S5与P2L_S5）。除前述人脸差异外，此类序列还存在持续遮挡情况，这为身份追踪与人脸验证带来了挑战。 该数据集可应用于（但不限于）以下研究方向： - 人员重识别（person re-identification） - 图像集合匹配（image set matching） - 人脸质量评估（face quality measurement） - 人脸聚类（face clustering） - 三维人脸重建（3D face reconstruction） - 行人/人脸追踪（pedestrian/face tracking） - 背景估计与背景减除（background estimation and subtraction） 若您在工作（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 <strong>Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition</strong> <em>IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops</em>，第81-88页，2011年。 http://doi.org/10.1109/CVPRW.2011.5981881