Generalizable Multi-Camera 3D Pedestrian Detection

• URL: http://arxiv.org/abs/2104.05813v1
• Date: Mon, 12 Apr 2021 20:58:25 GMT
• Title: Generalizable Multi-Camera 3D Pedestrian Detection
• Authors: Jo\~ao Paulo Lima, Rafael Roberto, Lucas Figueiredo, Francisco Sim\~oes, Veronica Teichrieb
• Abstract summary: We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene. We estimate pedestrian location on the ground plane using a novel based on human body poses and person's bounding boxes from an off-the-shelf monocular detector. We then project these locations onto the world ground plane and fuse them with a new formulation of a clique cover problem.
• Score: 1.8303072203996347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene. We estimate pedestrian location on the ground plane using a novel heuristic based on human body poses and person's bounding boxes from an off-the-shelf monocular detector. We then project these locations onto the world ground plane and fuse them with a new formulation of a clique cover problem. We also propose an optional step for exploiting pedestrian appearance during fusion by using a domain-generalizable person re-identification model. We evaluated the proposed approach on the challenging WILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior to state-of-the-art generalizable detection techniques.

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