TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes

• URL: http://arxiv.org/abs/2412.10308v1
• Date: Fri, 13 Dec 2024 17:42:53 GMT
• Title: TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes
• Authors: Yan Xia, Yunxiang Lu, Rui Song, Oussema Dhaouadi, João F. Henriques, Daniel Cremers,
• Abstract summary: We tackle the problem of localizing the traffic surveillance cameras in cooperative perception. We introduce a novel neural network, TrafficLoc, localizing traffic cameras within a 3D reference map. Our experiments show that our TrafficLoc improves the localization accuracy over the state-of-the-art Image-to-point cloud registration methods.
• Score: 49.43995864524434
• License:
• Abstract: We tackle the problem of localizing the traffic surveillance cameras in cooperative perception. To overcome the lack of large-scale real-world intersection datasets, we introduce Carla Intersection, a new simulated dataset with 75 urban and rural intersections in Carla. Moreover, we introduce a novel neural network, TrafficLoc, localizing traffic cameras within a 3D reference map. TrafficLoc employs a coarse-to-fine matching pipeline. For image-point cloud feature fusion, we propose a novel Geometry-guided Attention Loss to address cross-modal viewpoint inconsistencies. During coarse matching, we propose an Inter-Intra Contrastive Learning to achieve precise alignment while preserving distinctiveness among local intra-features within image patch-point group pairs. Besides, we introduce Dense Training Alignment with a soft-argmax operator to consider additional features when regressing the final position. Extensive experiments show that our TrafficLoc improves the localization accuracy over the state-of-the-art Image-to-point cloud registration methods by a large margin (up to 86%) on Carla Intersection and generalizes well to real-world data. TrafficLoc also achieves new SOTA performance on KITTI and NuScenes datasets, demonstrating strong localization ability across both in-vehicle and traffic cameras. Our project page is publicly available at https://tum-luk.github.io/projects/trafficloc/.

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