Learning Data Association for Multi-Object Tracking using Only Coordinates

• URL: http://arxiv.org/abs/2403.08018v1
• Date: Tue, 12 Mar 2024 18:36:18 GMT
• Title: Learning Data Association for Multi-Object Tracking using Only Coordinates
• Authors: Mehdi Miah, Guillaume-Alexandre Bilodeau, Nicolas Saunier
• Abstract summary: Transformer-based module TWiX is trained on sets of tracks with the objective of discriminating pairs of tracks coming from the same object from those which are not. By inserting TWiX within an online cascade matching pipeline, our tracker C-TWiX achieves state-of-the-art performance on the DanceTrack and KITTIMOT datasets.
• Score: 8.786893621311433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel Transformer-based module to address the data association problem for multi-object tracking. From detections obtained by a pretrained detector, this module uses only coordinates from bounding boxes to estimate an affinity score between pairs of tracks extracted from two distinct temporal windows. This module, named TWiX, is trained on sets of tracks with the objective of discriminating pairs of tracks coming from the same object from those which are not. Our module does not use the intersection over union measure, nor does it requires any motion priors or any camera motion compensation technique. By inserting TWiX within an online cascade matching pipeline, our tracker C-TWiX achieves state-of-the-art performance on the DanceTrack and KITTIMOT datasets, and gets competitive results on the MOT17 dataset. The code will be made available upon publication.

Related papers

• Bridging the Gap Between End-to-end and Non-End-to-end Multi-Object Tracking [27.74953961900086]
  Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not surpassed non-end-to-end tracking-by-detection methods. We present Co-MOT, a simple and effective method to facilitate e2e-MOT by a novel coopetition label assignment with a shadow concept.
  arXiv  Detail & Related papers  (2023-05-22T05:18:34Z)
• ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box [81.45219802386444]
  Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes. In 3D scenarios, it is much easier for the tracker to predict object velocities in the world coordinate.
  arXiv  Detail & Related papers  (2023-03-27T15:35:21Z)
• OmniTracker: Unifying Object Tracking by Tracking-with-Detection [119.51012668709502]
  OmniTracker is presented to resolve all the tracking tasks with a fully shared network architecture, model weights, and inference pipeline. Experiments on 7 tracking datasets, including LaSOT, TrackingNet, DAVIS16-17, MOT17, MOTS20, and YTVIS19, demonstrate that OmniTracker achieves on-par or even better results than both task-specific and unified tracking models.
  arXiv  Detail & Related papers  (2023-03-21T17:59:57Z)
• SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking [20.286114226299237]
  This paper introduces SMILEtrack, an innovative object tracker with a Siamese network-based Similarity Learning Module (SLM) The SLM calculates the appearance similarity between two objects, overcoming the limitations of feature descriptors in Separate Detection and Embedding models. Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames.
  arXiv  Detail & Related papers  (2022-11-16T10:49:48Z)
• 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds [95.54285993019843]
  We propose a method for joint detection and tracking of multiple objects in 3D point clouds. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
  arXiv  Detail & Related papers  (2022-11-01T20:59:38Z)
• Bag of Tricks for Domain Adaptive Multi-Object Tracking [4.084199842578325]
  The proposed method was built from pre-existing detector and tracker under the tracking-by-detection paradigm. The tracker we used is an online tracker that merely links newly received detections with existing tracks. Our method, SIA_Track, takes the first place on MOT Synth2MOT17 track at BMTT 2022 challenge.
  arXiv  Detail & Related papers  (2022-05-31T08:49:20Z)
• Improving tracking with a tracklet associator [17.839783649372116]
  Multiple object tracking (MOT) is a task in computer vision that aims to detect the position of objects in videos and to associate them to a unique identity. We propose an approach based on Constraint Programming (CP) whose goal is to be grafted to any existing tracker in order to improve its object association results.
  arXiv  Detail & Related papers  (2022-04-22T12:47:46Z)
• Unified Transformer Tracker for Object Tracking [58.65901124158068]
  We present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both Single Object Tracking (SOT) and Multiple Object Tracking (MOT)
  arXiv  Detail & Related papers  (2022-03-29T01:38:49Z)
• Hybrid Tracker with Pixel and Instance for Video Panoptic Segmentation [50.62685357414904]
  Video Panoptic coefficient (VPS) aims to generate coherent panoptic segmentation and track the identities of all pixels across video frames. We present HybridTracker, a lightweight and joint tracking model attempting to eliminate the limitations of the single tracker. Comprehensive experiments show that HybridTracker achieves superior performance than state-of-the-art methods on Cityscapes-VPS and VIPER datasets.
  arXiv  Detail & Related papers  (2022-03-02T16:21:55Z)
• Exploring Simple 3D Multi-Object Tracking for Autonomous Driving [10.921208239968827]
  3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and inevitably require a matching step for the detection association. We present SimTrack to simplify the hand-crafted tracking paradigm by proposing an end-to-end trainable model for joint detection and tracking from raw point clouds.
  arXiv  Detail & Related papers  (2021-08-23T17:59:22Z)
• Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking [102.31092931373232]
  We propose a simple online model named Chained-Tracker (CTracker), which naturally integrates all the three subtasks into an end-to-end solution. The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective.
  arXiv  Detail & Related papers  (2020-07-29T02:38:49Z)

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.