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

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          Abstract

          This paper 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 2D and 3D labels of pedestrians. We also present a novel 3D model fitting algorithm for automatic 3D labeling harnessing constraints across different modalities and novel shape and temporal priors. All annotated 3D pedestrians are localized into the real-world metric space, and the generated 3D models are validated using a mocap system configured in a controlled outdoor environment to simulate pedestrians in urban intersections. We also show that the manual 2D labels can be replaced by state-of-the-art automated labeling approaches, thereby facilitating automatic generation of large scale datasets.

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          Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments

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            Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video

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              Pose-conditioned joint angle limits for 3D human pose reconstruction

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                Author and article information

                Journal
                10 September 2018
                Article
                1809.03605
                a2dfdf02-07e7-4a2d-8503-b0e32dc0c960

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                cs.CV cs.RO

                Computer vision & Pattern recognition,Robotics
                Computer vision & Pattern recognition, Robotics

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