research-article

A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm

Authors:

Zhiyong YuAuthors Info & Claims

ICRAI '20: Proceedings of the 6th International Conference on Robotics and Artificial Intelligence

Pages 134 - 139

https://doi.org/10.1145/3449301.3449324

Published: 09 June 2021 Publication History

Abstract

At present, with the continuous development of single object tracking(SOT) tracker, more and more SOT tracker are applied to multi-object tracking(MOT) tasks. However, in the traditional method of affinity computation, affinity model is used as the metric of Hungarian algorithm, which has the low discrimination rate of similar objects and leads to ID switch easily. In order to solve this problem, we propose a cost matrix optimization method based on spatial constraints under Hungarian algorithm. In the data association stage, Kalman filter is used to estimate the motion vector of the object, so that the current position of the object can be linearly predicted. The weight of the cost matrix is modified according to the spatial relationship between the estimated position and the detection results, which is used for the subsequent re-identification task. It is worth noting that the above methods do not need extra training and can be directly used in other multi-object tracking models. Our method has been evaluated on MOT16(46.7%), MOT17(49.7%), and achieved the effect of SOTA. The entire results can be found on MOTChallenge website1 .

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Cited By

Zhang YChen H(2024)Multi-objective Pedestrian Tracking Based on Attention Multi-Scale Feature Fusion with Dynamic Sample2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)10.1109/ICECAI62591.2024.10675282(706-712)Online publication date: 31-May-2024
https://doi.org/10.1109/ICECAI62591.2024.10675282
Cao WWang XLiu XXu Y(2024)A deep learning framework for multi‐object tracking in team sports videosIET Computer Vision10.1049/cvi2.12266Online publication date: 2-Jan-2024
https://doi.org/10.1049/cvi2.12266

Index Terms

A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
2. Theory of computation
  1. Design and analysis of algorithms

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

ICRAI '20: Proceedings of the 6th International Conference on Robotics and Artificial Intelligence

November 2020

288 pages

ISBN:9781450388597

DOI:10.1145/3449301

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 June 2021

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National Natural Science Foundation of China

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ICRAI 2020

ICRAI 2020: 2020 6th International Conference on Robotics and Artificial Intelligence

November 20 - 22, 2020

Singapore, Singapore

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Cited By

Zhang YChen H(2024)Multi-objective Pedestrian Tracking Based on Attention Multi-Scale Feature Fusion with Dynamic Sample2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)10.1109/ICECAI62591.2024.10675282(706-712)Online publication date: 31-May-2024
https://doi.org/10.1109/ICECAI62591.2024.10675282
Cao WWang XLiu XXu Y(2024)A deep learning framework for multi‐object tracking in team sports videosIET Computer Vision10.1049/cvi2.12266Online publication date: 2-Jan-2024
https://doi.org/10.1049/cvi2.12266

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