HD Map Information for Three-Dimensional Multi-Object Tracking
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Kothari, Yash
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Abstract
The breakdown of 3DMOT into its sub-parts allows us to
identify gaps in the research that are applicable to our problem area. While the ability to detect
objects is being worked on passively through multiple studies (including ours), questions still
remain about how the algorithm of 3DMOT can be improved beyond just working on improving
the quality of 3-D Object Detection algorithms. Different ways to associate bounding boxes of
the same object and contrasting methods of estimating the motion of an object to understand
where to look for it in the future are both significant problems that showcase the lack of a robust
3DMOT algorithm that can handle both smooth and abrupt motion. Gao et al. (2020) provide a
unique method of encoding feature information from HD Maps (lane geometry information) that
might be a useful additional feature in providing 3DMOT algorithms with supervision on regions
of interest that may contain objects to be tracked. Overall, the process of improving 3DMOT
algorithms relies on improving association supervision, solving both smooth and robust motion
cases and providing a good estimate on how an object continues to move over time. Our method
incorporates the above ideologies and aims to utilize the information from HD Maps to improve
the performance of 3DMOT algorithms, with an emphasis on decreasing identity switches and
increasing multi-object tracking accuracy
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Undergraduate Research Option Thesis