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• W3136115421 startingPage "1992" @default.
• W3136115421 abstract "A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer’s actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform. The object association leverages quasi-dense similarity learning to identify objects in various poses and viewpoints with appearance cues only. After initial 2D association, we further utilize 3D bounding boxes depth-ordering heuristics for robust instance association and motion-based 3D trajectory prediction for re-identification of occluded vehicles. In the end, an LSTM-based object velocity learning module aggregates the long-term trajectory information for more accurate motion extrapolation. Experiments on our proposed simulation data and real-world benchmarks, including KITTI, nuScenes, and Waymo datasets, show that our tracking framework offers robust object association and tracking on urban-driving scenarios. On the Waymo Open benchmark, we establish the first camera-only baseline in the 3D tracking and 3D detection challenges. Our quasi-dense 3D tracking pipeline achieves impressive improvements on the nuScenes 3D tracking benchmark with near five times tracking accuracy of the best vision-only submission among all published methods." @default.
• W3136115421 created "2021-03-29" @default.
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• W3136115421 date "2023-02-01" @default.
• W3136115421 modified "2023-10-16" @default.
• W3136115421 title "Monocular Quasi-Dense 3D Object Tracking" @default.
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• W3136115421 doi "https://doi.org/10.1109/tpami.2022.3168781" @default.
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