UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking

• URL: http://arxiv.org/abs/2402.12303v2
• Date: Mon, 29 Apr 2024 20:35:09 GMT
• Title: UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking
• Authors: Chang Won Lee, Steven L. Waslander,
• Abstract summary: Multi-object tracking (MOT) methods have seen a significant boost in performance recently. We introduce UncertaintyTrack, a collection of extensions that can be applied to multiple TBD trackers. Experiments on the Berkeley Deep Drive MOT dataset show that the combination of our method and informative uncertainty estimates reduces the number of ID switches by around 19%.
• Score: 8.645078288584305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-object tracking (MOT) methods have seen a significant boost in performance recently, due to strong interest from the research community and steadily improving object detection methods. The majority of tracking methods follow the tracking-by-detection (TBD) paradigm, blindly trust the incoming detections with no sense of their associated localization uncertainty. This lack of uncertainty awareness poses a problem in safety-critical tasks such as autonomous driving where passengers could be put at risk due to erroneous detections that have propagated to downstream tasks, including MOT. While there are existing works in probabilistic object detection that predict the localization uncertainty around the boxes, no work in 2D MOT for autonomous driving has studied whether these estimates are meaningful enough to be leveraged effectively in object tracking. We introduce UncertaintyTrack, a collection of extensions that can be applied to multiple TBD trackers to account for localization uncertainty estimates from probabilistic object detectors. Experiments on the Berkeley Deep Drive MOT dataset show that the combination of our method and informative uncertainty estimates reduces the number of ID switches by around 19\% and improves mMOTA by 2-3%. The source code is available at https://github.com/TRAILab/UncertaintyTrack

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• UTrack: Multi-Object Tracking with Uncertain Detections [37.826006378381955]
  We introduce, for the first time, a fast way to obtain the empirical predictive distribution during object detection. Our mechanism can easily be integrated into state-of-the-art trackers, enabling them to fully exploit the uncertainty in the detections. We demonstrate the effectiveness of our contribution on a variety of benchmarks, such as MOT17, MOT20, DanceTrack, and KITTI.
  arXiv  Detail & Related papers  (2024-08-30T08:34:51Z)
• UA-Track: Uncertainty-Aware End-to-End 3D Multi-Object Tracking [37.857915442467316]
  3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have shown promising potential for the 3D MOT task. Existing methods overlook the uncertainty issue, which refers to the lack of precise confidence about the state and location of tracked objects. We propose an Uncertainty-Aware 3D MOT framework, UA-Track, which tackles the uncertainty problem from multiple aspects.
  arXiv  Detail & Related papers  (2024-06-04T09:34:46Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Uncertainty-aware Unsupervised Multi-Object Tracking [33.53331700312752]
  unsupervised multi-object trackers are inferior to learning reliable feature embeddings. Recent self-supervised techniques are adopted, whereas they failed to capture temporal relations. This paper argues that though the uncertainty problem is inevitable, it is possible to leverage the uncertainty itself to improve the learned consistency in turn.
  arXiv  Detail & Related papers  (2023-07-28T09:03:06Z)
• Uncertainty-Aware AB3DMOT by Variational 3D Object Detection [74.8441634948334]
  Uncertainty estimation is an effective tool to provide statistically accurate predictions. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty.
  arXiv  Detail & Related papers  (2023-02-12T14:30:03Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)
• Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection [26.008419879970365]
  In this work, we infer the uncertainty in bounding box labels from LiDAR point clouds based on a generative model. Comprehensive experiments show that the proposed model reflects complex environmental noises in LiDAR perception and the label quality. We propose Jaccard IoU as a new evaluation metric that extends IoU by incorporating label uncertainty.
  arXiv  Detail & Related papers  (2020-12-18T09:11:44Z)
• Detecting Invisible People [58.49425715635312]
  We re-purpose tracking benchmarks and propose new metrics for the task of detecting invisible objects. We demonstrate that current detection and tracking systems perform dramatically worse on this task. Second, we build dynamic models that explicitly reason in 3D, making use of observations produced by state-of-the-art monocular depth estimation networks.
  arXiv  Detail & Related papers  (2020-12-15T16:54:45Z)
• Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss [13.346392746224117]
  Uncertainty helps us tackle the error in the perception system and improve robustness. We propose a method for improving target tracking performance by adding uncertainty regression to the SECOND detector.
  arXiv  Detail & Related papers  (2020-11-04T21:53:31Z)
• Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets [96.98888948518815]
  State-of-the-art multi-object tracking(MOT) methods follow the tracking-by-detection paradigm. We propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes.
  arXiv  Detail & Related papers  (2020-07-18T19:51:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.