| Autor: |
Xu, Fengyu, Wang, Zhiling, Wang, Hanqi, Lin, Linglong, Liang, Huawei |
| Předmět: |
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| Zdroj: |
Applied Intelligence; Jan2023, Vol. 53 Issue 2, p2362-2390, 29p |
| Abstrakt: |
This paper presents a novel dynamic vehicle tracking framework, achieving accurate pose estimation and tracking in urban environments. For vehicle tracking with laser scanners, pose estimation extracts geometric information of the target from a point cloud clustering unit, which plays an essential role in tracking tasks. However, the point cloud acquired from laser scanners only provides distance measurements to the object surface facing the sensor, leading to nonnegligible pose estimation errors. To address this issue, we take the motion information of targets as feedback to assist vehicle detection and pose estimation. In addition, the heading normalization vehicle model and a robust target size estimation method are introduced to deduce the pose of a vehicle with 2D matched filtering. Furthermore, considering the mobility of vehicles, we utilize the interactive multitude model (IMM) to capture multiple motion patterns. Compared to existing methods in the literature, our method can be applied to spatially sparse or incomplete point cloud observations. Experimental results demonstrate that our vehicle tracking framework achieves promising performance, and its real-time capability is also validated in real traffic scenarios. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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