GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021

• URL: http://arxiv.org/abs/2202.11983v1
• Date: Thu, 24 Feb 2022 09:42:00 GMT
• Title: GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021
• Authors: Yunhao Du, Junfeng Wan, Yanyun Zhao, Binyu Zhang, Zhihang Tong, Junhao Dong
• Abstract summary: We propose a new multiple object tracker, named GIAOTracker. It consists of three stages, i.e., online tracking, global link and post-processing. With the effectiveness of the three stages, GIAOTracker achieves state-of-the-art performance on the VisDrone MOT dataset.
• Score: 6.4515884598231
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, algorithms for multiple object tracking tasks have benefited from great progresses in deep models and video quality. However, in challenging scenarios like drone videos, they still suffer from problems, such as small objects, camera movements and view changes. In this paper, we propose a new multiple object tracker, which employs Global Information And some Optimizing strategies, named GIAOTracker. It consists of three stages, i.e., online tracking, global link and post-processing. Given detections in every frame, the first stage generates reliable tracklets using information of camera motion, object motion and object appearance. Then they are associated into trajectories by exploiting global clues and refined through four post-processing methods. With the effectiveness of the three stages, GIAOTracker achieves state-of-the-art performance on the VisDrone MOT dataset and wins the 3rd place in the VisDrone2021 MOT Challenge.

Related papers

• The 8th AI City Challenge [57.25825945041515]
  The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks.
  arXiv  Detail & Related papers  (2024-04-15T03:12:17Z)
• ReIDTracker Sea: the technical report of BoaTrack and SeaDronesSee-MOT challenge at MaCVi of WACV24 [0.0]
  Our solution tries to explore Multi-Object Tracking in maritime Unmanned Aerial vehicles (UAVs) and Unmanned Surface Vehicles (USVs) usage scenarios. The scheme achieved top 3 performance on both UAV-based Multi-Object Tracking with Reidentification and USV-based Multi-Object Tracking benchmarks.
  arXiv  Detail & Related papers  (2023-11-12T07:37:07Z)
• Tracking Anything in High Quality [63.63653185865726]
  HQTrack is a framework for High Quality Tracking anything in videos. It consists of a video multi-object segmenter (VMOS) and a mask refiner (MR)
  arXiv  Detail & Related papers  (2023-07-26T06:19:46Z)
• 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)
• CXTrack: Improving 3D Point Cloud Tracking with Contextual Information [59.55870742072618]
  3D single object tracking plays an essential role in many applications, such as autonomous driving. We propose CXTrack, a novel transformer-based network for 3D object tracking. We show that CXTrack achieves state-of-the-art tracking performance while running at 29 FPS.
  arXiv  Detail & Related papers  (2022-11-12T11:29:01Z)
• InterTrack: Interaction Transformer for 3D Multi-Object Tracking [9.283656931246645]
  3D multi-object tracking (MOT) is a key problem for autonomous vehicles. Our proposed solution, InterTrack, generates discriminative object representations for data association. We validate our approach on the nuScenes 3D MOT benchmark, where we observe significant improvements.
  arXiv  Detail & Related papers  (2022-08-17T03:24:36Z)
• Single Object Tracking Research: A Survey [44.24280758718638]
  This paper presents the rationale and works of two most popular tracking frameworks in past ten years. We present some deep learning based tracking methods categorized by different network structures. We also introduce some classical strategies for handling the challenges in tracking problem.
  arXiv  Detail & Related papers  (2022-04-25T02:59:15Z)
• MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking [72.76685780516371]
  We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community. We provide a categorization of state-of-the-art trackers and a broad error analysis.
  arXiv  Detail & Related papers  (2020-10-15T06:52:16Z)
• Detection and Tracking Meet Drones Challenge [131.31749447313197]
  This paper presents a review of object detection and tracking datasets and benchmarks, and discusses the challenges of collecting large-scale drone-based object detection and tracking datasets with manual annotations. We describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions.
  arXiv  Detail & Related papers  (2020-01-16T00:11:56Z)

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.