SMOT: Single-Shot Multi Object Tracking
- URL: http://arxiv.org/abs/2010.16031v1
- Date: Fri, 30 Oct 2020 02:46:54 GMT
- Title: SMOT: Single-Shot Multi Object Tracking
- Authors: Wei Li, Yuanjun Xiong, Shuo Yang, Siqi Deng, Wei Xia
- Abstract summary: 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.
- Score: 39.34493475666044
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present single-shot multi-object tracker (SMOT), a new tracking framework
that converts any single-shot detector (SSD) model into an online multiple
object tracker, which emphasizes simultaneously detecting and tracking of the
object paths. Contrary to the existing tracking by detection approaches which
suffer from errors made by the object detectors, SMOT adopts the recently
proposed scheme of tracking by re-detection. We combine this scheme with SSD
detectors by proposing a novel tracking anchor assignment module. With this
design SMOT is able to generate tracklets with a constant per-frame runtime. A
light-weighted linkage algorithm is then used for online tracklet linking. On
three benchmarks of object tracking: Hannah, Music Videos, and MOT17, the
proposed SMOT achieves state-of-the-art performance.
Related papers
- HSTrack: Bootstrap End-to-End Multi-Camera 3D Multi-object Tracking with Hybrid Supervision [34.7347336548199]
In camera-based 3D multi-object tracking (MOT), the prevailing methods follow the tracking-by-query-propagation paradigm.
We present HSTrack, a novel plug-and-play method designed to co-facilitate multi-task learning for detection and tracking.
arXiv Detail & Related papers (2024-11-11T08:18:49Z) - 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) - ADA-Track: End-to-End Multi-Camera 3D Multi-Object Tracking with Alternating Detection and Association [15.161640917854363]
We introduce ADA-Track, a novel end-to-end framework for 3D MOT from multi-view cameras.
We introduce a learnable data association module based on edge-augmented cross-attention.
We integrate this association module into the decoder layer of a DETR-based 3D detector.
arXiv Detail & Related papers (2024-05-14T19:02:33Z) - TrajectoryFormer: 3D Object Tracking Transformer with Predictive
Trajectory Hypotheses [51.60422927416087]
3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots.
We present TrajectoryFormer, a novel point-cloud-based 3D MOT framework.
arXiv Detail & Related papers (2023-06-09T13:31:50Z) - 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) - 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) - Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT
Philosophy [63.91005999481061]
A practical long-term tracker typically contains three key properties, i.e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism.
We propose a two-task tracking frame work (named DMTrack) to achieve distractor-aware fast tracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT) philosophy.
Our tracker achieves state-of-the-art performance on the LaSOT, OxUvA, TLP, VOT2018LT and VOT 2019LT benchmarks and runs in real-time (3x faster
arXiv Detail & Related papers (2021-04-25T00:59:53Z) - Track to Detect and Segment: An Online Multi-Object Tracker [81.15608245513208]
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.
arXiv Detail & Related papers (2021-03-16T02:34:06Z) - DEFT: Detection Embeddings for Tracking [3.326320568999945]
We propose an efficient joint detection and tracking model named DEFT.
Our approach relies on an appearance-based object matching network jointly-learned with an underlying object detection network.
DEFT has comparable accuracy and speed to the top methods on 2D online tracking leaderboards.
arXiv Detail & Related papers (2021-02-03T20:00:44Z)
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.