2021/04/29 by Aleksandr Kim, Kim, Aleksandr, Aljoša Ošep +3 · 9 citations
Computer Science · Engineering · #Advanced Neural Network Applications #Artificial intelligence #Code (set theory) #Computer science #Computer vision #Engineering #Geography #Lidar #Mobile robot #Object (grammar) #Object detection #Range (aeronautics) #Remote sensing #Robot #Robotics and Sensor-Based Localization #SIGNAL (programming language) #Segmentation #Tracking (education) #Trajectory #Video Surveillance and Tracking Methods #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.2104.14682
published in arXiv (Cornell University) (Cornell University) · To be published at ICRA 2021. Source code available at https://github.com/aleksandrkim61/EagerMOT
arxiv created 2021/04/29 · openalex publication_date 2021/04/29 · arxiv updated 2021/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.