Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones
Tracking moving objects in a city, such as suspicious vehicles or persons, is important for public safety management. Traditionally, target tracking is assisted by the pre-deployed stationary surveillance cameras, which are with insufficient coverage. In this work, we propose a different approach ca...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9031332/ |
id |
doaj-14381d2ec2a94320ae356af714883a79 |
---|---|
record_format |
Article |
spelling |
doaj-14381d2ec2a94320ae356af714883a792021-03-30T01:33:57ZengIEEEIEEE Access2169-35362020-01-018925919260210.1109/ACCESS.2020.29799339031332Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile PhonesZhiyong Yu0https://orcid.org/0000-0002-2051-9462Lei Han1https://orcid.org/0000-0002-7389-782XQi An2Huihui Chen3Houchun Yin4Zhiwen Yu5College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou, ChinaCollege of Mathematics and Computer Sciences, Fuzhou University, Fuzhou, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaSchool of Electronic and Information Engineering, Foshan University, Foshan, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaTracking moving objects in a city, such as suspicious vehicles or persons, is important for public safety management. Traditionally, target tracking is assisted by the pre-deployed stationary surveillance cameras, which are with insufficient coverage. In this work, we propose a different approach called Co-Tracking, a real-time target tracking system that leverages both citizens' mobile phones and stationary surveillance cameras to track moving objects collaboratively. Two key techniques are focused. Firstly, in order to accurately assign tracking tasks, we propose the Middle Query Location Prediction (MQLP) algorithm for predicting the target's location. Secondly, in order to efficiently utilizes these human/machine resources, we propose a heuristic algorithm, namely S-Maximum, to optimize the task allocation, including maximizing the number of completed tracking tasks and minimizing the number of mobile phones. Experimental results show that the proposed Co-Tracking system can effectively track moving objects with low incentive costs.https://ieeexplore.ieee.org/document/9031332/Mobile crowdsensinglocation predictiontarget trackingcollaborative sensing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhiyong Yu Lei Han Qi An Huihui Chen Houchun Yin Zhiwen Yu |
spellingShingle |
Zhiyong Yu Lei Han Qi An Huihui Chen Houchun Yin Zhiwen Yu Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones IEEE Access Mobile crowdsensing location prediction target tracking collaborative sensing |
author_facet |
Zhiyong Yu Lei Han Qi An Huihui Chen Houchun Yin Zhiwen Yu |
author_sort |
Zhiyong Yu |
title |
Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones |
title_short |
Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones |
title_full |
Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones |
title_fullStr |
Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones |
title_full_unstemmed |
Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones |
title_sort |
co-tracking: target tracking via collaborative sensing of stationary cameras and mobile phones |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Tracking moving objects in a city, such as suspicious vehicles or persons, is important for public safety management. Traditionally, target tracking is assisted by the pre-deployed stationary surveillance cameras, which are with insufficient coverage. In this work, we propose a different approach called Co-Tracking, a real-time target tracking system that leverages both citizens' mobile phones and stationary surveillance cameras to track moving objects collaboratively. Two key techniques are focused. Firstly, in order to accurately assign tracking tasks, we propose the Middle Query Location Prediction (MQLP) algorithm for predicting the target's location. Secondly, in order to efficiently utilizes these human/machine resources, we propose a heuristic algorithm, namely S-Maximum, to optimize the task allocation, including maximizing the number of completed tracking tasks and minimizing the number of mobile phones. Experimental results show that the proposed Co-Tracking system can effectively track moving objects with low incentive costs. |
topic |
Mobile crowdsensing location prediction target tracking collaborative sensing |
url |
https://ieeexplore.ieee.org/document/9031332/ |
work_keys_str_mv |
AT zhiyongyu cotrackingtargettrackingviacollaborativesensingofstationarycamerasandmobilephones AT leihan cotrackingtargettrackingviacollaborativesensingofstationarycamerasandmobilephones AT qian cotrackingtargettrackingviacollaborativesensingofstationarycamerasandmobilephones AT huihuichen cotrackingtargettrackingviacollaborativesensingofstationarycamerasandmobilephones AT houchunyin cotrackingtargettrackingviacollaborativesensingofstationarycamerasandmobilephones AT zhiwenyu cotrackingtargettrackingviacollaborativesensingofstationarycamerasandmobilephones |
_version_ |
1724186745312903168 |