@inproceedings{8df2b186c27d4f3b8dd3ca0345ba6c48,
title = "3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds",
abstract = "We propose a method for joint detection and tracking of multiple objects in 3D point clouds, a task conventionally treated as a two-step process comprising object detection followed by data association. Our method embeds both steps into a single end-to-end trainable network eliminating the dependency on external object detectors. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network, thereby making it a utilitarian formulation for real-world scenarios. Computing affinity matrix by employing features similarity across consecutive point cloud scans forms an integral part of visual tracking. We propose an attention-based refinement module to refine the affinity matrix by suppressing erroneous correspondences. The module is designed to capture the global context in affinity matrix by employing self-attention within each affinity matrix and cross-attention across a pair of affinity matrices. Unlike competing approaches, our network does not require complex post-processing algorithms, and directly processes raw LiDAR frames to output tracking results. We demonstrate the effectiveness of our method on three tracking benchmarks: JRDB, Waymo, and KITTI. Experimental evaluations indicate the ability of our model to generalize well across datasets.",
author = "Jyoti Kini and Ajmal Mian and Mubarak Shah",
note = "Funding Information: *This work was conducted at UCF and supported by Lockheed Martin Corporate Engineering, Technology and Operations (CETO) University Engagement (UE) - Research. Professor Ajmal Mian is the recipient of an Australian Research Council Future Fellowship Award (project number FT210100268) funded by the Australian Government. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 ; Conference date: 29-05-2023 Through 02-06-2023",
year = "2023",
doi = "10.1109/ICRA48891.2023.10160305",
language = "English",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "841--848",
booktitle = "Proceedings - ICRA 2023",
address = "United States",
}