Track to Detect and Segment: An Online Multi-Object Tracker

• URL: http://arxiv.org/abs/2103.08808v1
• Date: Tue, 16 Mar 2021 02:34:06 GMT
• Title: Track to Detect and Segment: An Online Multi-Object Tracker
• Authors: Jialian Wu, Jiale Cao, Liangchen Song, Yu Wang, Ming Yang, Junsong Yuan
• Abstract summary: TraDeS is an online joint detection and tracking model, exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking offset by a cost volume, which is used to propagate previous object features.
• Score: 81.15608245513208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking. In this paper, we present a new online joint detection and tracking model, TraDeS (TRAck to DEtect and Segment), exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking offset by a cost volume, which is used to propagate previous object features for improving current object detection and segmentation. Effectiveness and superiority of TraDeS are shown on 4 datasets, including MOT (2D tracking), nuScenes (3D tracking), MOTS and Youtube-VIS (instance segmentation tracking). Project page: https://jialianwu.com/projects/TraDeS.html.

Related papers

• Lost and Found: Overcoming Detector Failures in Online Multi-Object Tracking [15.533652456081374]
  Multi-object tracking (MOT) endeavors to precisely estimate identities and positions of multiple objects over time. Modern detectors may occasionally miss some objects in certain frames, causing trackers to cease tracking prematurely. We propose BUSCA, meaning to search', a versatile framework compatible with any online TbD system.
  arXiv  Detail & Related papers  (2024-07-14T10:45:12Z)
• 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)
• DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes [74.64897845999677]
  We introduce a new cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians. Our DIVOTrack has fifteen distinct scenarios and 953 cross-view tracks, surpassing all cross-view multi-object tracking datasets currently available. Furthermore, we provide a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT.
  arXiv  Detail & Related papers  (2023-02-15T14:10:42Z)
• 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)
• 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)
• A Discriminative Single-Shot Segmentation Network for Visual Object Tracking [13.375369415113534]
  We propose a discriminative single-shot segmentation tracker -- D3S2. A single-shot network applies two target models with complementary geometric properties. D3S2 outperforms the leading segmentation tracker SiamMask on video object segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-12-22T12:48:51Z)
• TrackFormer: Multi-Object Tracking with Transformers [92.25832593088421]
  TrackFormer is an end-to-end multi-object tracking and segmentation model based on an encoder-decoder Transformer architecture. New track queries are spawned by the DETR object detector and embed the position of their corresponding object over time. TrackFormer achieves a seamless data association between frames in a new tracking-by-attention paradigm.
  arXiv  Detail & Related papers  (2021-01-07T18:59:29Z)
• Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving [22.693895321632507]
  We propose a probabilistic, multi-modal, multi-object tracking system consisting of different trainable modules. We show that our method outperforms current state-of-the-art on the NuScenes Tracking dataset.
  arXiv  Detail & Related papers  (2020-12-26T15:00:54Z)
• SMOT: Single-Shot Multi Object Tracking [39.34493475666044]
  Single-shot multi-object tracker (SMOT) is a new tracking framework that converts any single-shot detector (SSD) model into an online multiple object tracker. On three benchmarks of object tracking: Hannah, Music Videos, and MOT17, the proposed SMOT achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-30T02:46:54Z)

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.