Multiple Object Trackers in OpenCV: A Benchmark
- URL: http://arxiv.org/abs/2110.05102v1
- Date: Mon, 11 Oct 2021 09:12:02 GMT
- Title: Multiple Object Trackers in OpenCV: A Benchmark
- Authors: Na{\dj}a Dardagan, Adnan Br{\dj}anin, D\v{z}emil D\v{z}igal, Amila
Akagic
- Abstract summary: In this paper, we evaluate 7 trackers implemented in OpenCV against the MOT20 dataset.
The results are shown based on Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) metrics.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Object tracking is one of the most important and fundamental disciplines of
Computer Vision. Many Computer Vision applications require specific object
tracking capabilities, including autonomous and smart vehicles, video
surveillance, medical treatments, and many others. The OpenCV as one of the
most popular libraries for Computer Vision includes several hundred Computer
Vision algorithms. Object tracking tasks in the library can be roughly
clustered in single and multiple object trackers. The library is widely used
for real-time applications, but there are a lot of unanswered questions such as
when to use a specific tracker, how to evaluate its performance, and for what
kind of objects will the tracker yield the best results? In this paper, we
evaluate 7 trackers implemented in OpenCV against the MOT20 dataset. The
results are shown based on Multiple Object Tracking Accuracy (MOTA) and
Multiple Object Tracking Precision (MOTP) metrics.
Related papers
- Tracking Reflected Objects: A Benchmark [12.770787846444406]
We introduce TRO, a benchmark specifically for Tracking Reflected Objects.
TRO includes 200 sequences with around 70,000 frames, each carefully annotated with bounding boxes.
To provide a stronger baseline, we propose a new tracker, HiP-HaTrack, which uses hierarchical features to improve performance.
arXiv Detail & Related papers (2024-07-07T02:22:45Z) - Machine Learning Based Object Tracking [0.6466206145151128]
Authors were able to set a range of interest around an object using Open Computer Vision.
Next a tracking algorithm has been used to maintain tracking on an object while simultaneously operating two servo motors to keep the object centered in the frame.
arXiv Detail & Related papers (2024-01-15T19:46:05Z) - 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) - 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) - 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) - 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) - 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) - TAO: A Large-Scale Benchmark for Tracking Any Object [95.87310116010185]
Tracking Any Object dataset consists of 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average.
We ask annotators to label objects that move at any point in the video, and give names to them post factum.
Our vocabulary is both significantly larger and qualitatively different from existing tracking datasets.
arXiv Detail & Related papers (2020-05-20T21:07:28Z) - MOT20: A benchmark for multi object tracking in crowded scenes [73.92443841487503]
We present our MOT20benchmark, consisting of 8 new sequences depicting very crowded challenging scenes.
The benchmark was presented first at the 4thBMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR)
arXiv Detail & Related papers (2020-03-19T20:08:24Z)
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.