UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network

• URL: http://arxiv.org/abs/2503.12888v1
• Date: Mon, 17 Mar 2025 07:33:16 GMT
• Title: UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network
• Authors: Siyuan Yao, Yang Guo, Yanyang Yan, Wenqi Ren, Xiaochun Cao,
• Abstract summary: UncTrack is a novel uncertainty-aware transformer tracker that predicts the target localization uncertainty.<n>Our method outperforms other state-of-the-art methods in experiments.
• Score: 75.9933952886197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based trackers have achieved promising success and become the dominant tracking paradigm due to their accuracy and efficiency. Despite the substantial progress, most of the existing approaches tackle object tracking as a deterministic coordinate regression problem, while the target localization uncertainty has been greatly overlooked, which hampers trackers' ability to maintain reliable target state prediction in challenging scenarios. To address this issue, we propose UncTrack, a novel uncertainty-aware transformer tracker that predicts the target localization uncertainty and incorporates this uncertainty information for accurate target state inference. Specifically, UncTrack utilizes a transformer encoder to perform feature interaction between template and search images. The output features are passed into an uncertainty-aware localization decoder (ULD) to coarsely predict the corner-based localization and the corresponding localization uncertainty. Then the localization uncertainty is sent into a prototype memory network (PMN) to excavate valuable historical information to identify whether the target state prediction is reliable or not. To enhance the template representation, the samples with high confidence are fed back into the prototype memory bank for memory updating, making the tracker more robust to challenging appearance variations. Extensive experiments demonstrate that our method outperforms other state-of-the-art methods. Our code is available at https://github.com/ManOfStory/UncTrack.

Related papers

• 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)
• RTracker: Recoverable Tracking via PN Tree Structured Memory [71.05904715104411]
  We propose a recoverable tracking framework, RTracker, that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery. Specifically, we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss.
  arXiv  Detail & Related papers  (2024-03-28T08:54:40Z)
• UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking [8.645078288584305]
  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%.
  arXiv  Detail & Related papers  (2024-02-19T17:27:04Z)
• Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking [9.156625199253947]
  ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects. We introduce a novel and adaptive approach to differentiate between high and low-confidence detections.
  arXiv  Detail & Related papers  (2023-12-04T06:01:46Z)
• 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)
• Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding [121.66374635092097]
  Trajectories obtained from object detection and tracking are inevitably noisy. We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
  arXiv  Detail & Related papers  (2022-02-03T09:09:56Z)
• Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking [82.34356879078955]
  We propose a compact memory embedding to enhance the discrimination of the segmentation-based deformable visual tracking method. Our method outperforms the excellent segmentation-based trackers, i.e., D3S and SiamMask on DAVIS 2017 benchmark.
  arXiv  Detail & Related papers  (2021-11-23T03:07:12Z)
• 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)
• 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)

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.