Multiple object tracking: A literature review

@article{Luo2021MultipleOT,
  title={Multiple object tracking: A literature review},
  author={Wenhan Luo and Junliang Xing and Anton Milan and Xiaoqin Zhang and Wei Liu and Xiaowei Zhao and Tae-Kyun Kim},
  journal={Artif. Intell.},
  year={2021},
  volume={293},
  pages={103448}
}
Multiple Object Tracking (MOT) is an important computer vision problem which has gained increasing attention due to its academic and commercial potential. [...] Key Method 2) Instead of enumerating individual works, we discuss existing approaches according to various aspects, in each of which methods are divided into different groups and each group is discussed in detail for the principles, advances and drawbacks. 3) We examine experiments of existing publications and summarize results on popular datasets to…Expand
Multiple Object Tracking in Deep Learning Approaches: A Survey
  • Yesul Park, L. Dang, Sujin Lee, Dongil Han, Hyeonjoon Moon
  • Computer Science
  • Electronics
  • 2021
TLDR
This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. Expand
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TLDR
This paper presents a low-cost multiple object tracking technique by employing a novel appearance update model for object appearance modeling using K-means, which reduces the computational cost of the state-of-the-art MOT 6-fold by exploiting this facet of persistent appearance over the sequence of frames. Expand
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An easy-use multi-object tracking method based on bounding boxes and object appearance features that can tracking the multi- object accurately and can be easily used in practice is proposed. Expand
K-means based multiple objects tracking with long-term occlusion handling
TLDR
The proposed multiple objects tracking approach has the capability to deal long term and complete occlusion without any prior training of the shape and motion model of the objects and is cost effective in terms of memory and/or computation as compared with that of the existing state-of-the-art techniques. Expand
Joint appearance and motion model for multi-class multi-object tracking
TLDR
This work proposes to use both appearance and motion models, and to learn them jointly using graphical models and deep neural networks features, and introduces an indicator variable to predict sudden appearance change and/or occlusion. Expand
Single Object Tracking through a Fast and Effective Single-Multiple Model Convolutional Neural Network
TLDR
A special architecture is proposed based on which in contrast to the previous approaches, it is possible to identify the object location in a single shot while taking its template into account to distinguish it from the similar objects in the same area. Expand
Deep Affinity Network for Multiple Object Tracking
TLDR
The proposed Deep Affinity Network (DAN) learns compact, yet comprehensive features of pre-detected objects at several levels of abstraction, and performs exhaustive pairing permutations of those features in any two frames to infer object affinities. Expand
Training Algorithms for Multiple Object Tracking
TLDR
This thesis proposes a model that tracks both types of objects simultaneously, while respecting the physical laws of ball motion when in free fall, and interaction constraints that appear when players are in the possession of the ball. Expand
ReMOT: A model-agnostic refinement for multiple object tracking
TLDR
A Refining MOT Framework (ReMOT), which first splits imperfect tracklets and then merges the split tracklets with appearance features improved by self-supervised learning, is proposed, which can make significant improvements to state-of-the-art MOT results as powerful post-processing. Expand
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References

SHOWING 1-10 OF 269 REFERENCES
A survey of appearance models in visual object tracking
TLDR
This survey provides a detailed review of the existing 2D appearance models for visual object tracking and takes a module-based architecture that enables readers to easily grasp the key points ofVisual object tracking. Expand
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
TLDR
This work introduces two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. Expand
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
TLDR
With MOTChallenge, the work toward a novel multiple object tracking benchmark aimed to address issues of standardization, and the way toward a unified evaluation framework for a more meaningful quantification of multi-target tracking is described. Expand
Object tracking: A survey
TLDR
The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends to discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects. Expand
Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph
TLDR
A novel data association approach based on undirected hierarchical relation hypergraph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed Undirected affinity graph and makes the tracker robust to the spatially close targets with similar appearance. Expand
Detection and Tracking of Large Number of Targets in Wide Area Surveillance
TLDR
This paper divides the scene into grid cells, solves the tracking problem optimally within each cell using bipartite graph matching and then link tracks across cells, and uses median background modeling which requires few frames to obtain a workable model. Expand
Online Object Tracking: A Benchmark
TLDR
Large scale experiments are carried out with various evaluation criteria to identify effective approaches for robust tracking and provide potential future research directions in this field. Expand
Generic Object Crowd Tracking by Multi-Task Learning
TLDR
This work decomposes this problem into two main tasks, detection and tracking, and formulate them under the Multiple Task Learning (MTL) framework, where a binary detector is learnt to detect objects in images, whilst multiple trackers are learnt on top of the detector by MTL to trace detected objects in subsequent frames. Expand
Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism
TLDR
A CNN-based framework for online MOT that utilizes the merits of single object trackers in adapting appearance models and searching for target in the next frame and introduces spatial-temporal attention mechanism (STAM) to handle the drift caused by occlusion and interaction among targets. Expand
MOT16: A Benchmark for Multi-Object Tracking
TLDR
A new release of the MOTChallenge benchmark, which focuses on multiple people tracking, and offers a significant increase in the number of labeled boxes, but also provides multiple object classes beside pedestrians and the level of visibility for every single object of interest. Expand
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