PedX: Benchmark Dataset for Metric 3-D Pose Estimation of Pedestrians in Complex Urban Intersections

• Published in: IEEE robotics and automation letters 2019-04, Vol.4 (2), p.1940-1947
• Main Authors: Kim, Wonhui, Ramanagopal, Manikandasriram Srinivasan, Barto, Charles, Yu, Ming-Yuan, Rosaen, Karl, Goumas, Nick, Vasudevan, Ram, Johnson-Roberson, Matthew
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Cameras; Computer simulation; Computer vision for transportation; Datasets; human detection and tracking; Image resolution; Labeling; Labels; Laser radar; Measurement; Metric space; Motion capture; Pedestrians; Pose estimation; Solid modeling; Three dimensional models; Three-dimensional displays; Traffic intersections; Two dimensional displays
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2019.2896705

This letter presents a novel dataset titled PedX , a large-scale multimodal collection of pedestrians at complex urban intersections. PedX consists of more than 5 000 pairs of high-resolution (12MP) stereo images and LiDAR data along with providing two-dimensional (2-D) image labels and 3-D labels of pedestrians in a global coordinate frame. Data were captured at three four-way stop intersections with heavy pedestrian-vehicle interaction. We also present a 3-D model fitting algorithm for automatic labeling harnessing constraints across different modalities and novel shape and temporal priors. All annotated 3-D pedestrians are localized into the real-world metric space, and the generated 3-D models are validated using a motion capture system configured in a controlled outdoor environment to simulate pedestrians in urban intersections. We also show that the manual 2-D image labels can be replaced by state-of-the-art automated labeling approaches, thereby facilitating automatic generation of large scale datasets.