EagerMOT: 3D Multi-Object Tracking via Sensor Fusion
arXiv:2104.14682
Abstract
Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time. Existing methods rely on depth sensors (e.g., LiDAR) to detect and track targets in 3D space, but only up to a limited sensing range due to the sparsity of the signal. On the other hand, cameras provide a dense and rich visual signal that helps to localize even distant objects, but only in the image domain. In this paper, we propose EagerMOT, a simple tracking formulation that eagerly integrates all available object observations from both sensor modalities to obtain a well-informed interpretation of the scene dynamics. Using images, we can identify distant incoming objects, while depth estimates allow for precise trajectory localization as soon as objects are within the depth-sensing range. With EagerMOT, we achieve state-of-the-art results across several MOT tasks on the KITTI and NuScenes datasets. Our code is available at https://github.com/aleksandrkim61/EagerMOT.
arXiv commentTo be published at ICRA 2021. Source code available at https://github.com/aleksandrkim61/EagerMOT
References in corpus (10)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- MMDetection: Open MMLab Detection Toolbox and Benchmark
- Argoverse: 3D Tracking and Forecasting with Rich Maps
- End-to-end Learning of Multi-sensor 3D Tracking by Detection
- Track to Reconstruct and Reconstruct to Track
- Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
- Center-based 3D Object Detection and Tracking
- Joint Monocular 3D Vehicle Detection and Tracking
- Probabilistic 3D Multi-Object Tracking for Autonomous Driving
- How To Train Your Deep Multi-Object Tracker