Online video streaming for human tracking based on weighted resampling particle filter

This paper proposes a weighted resampling method for particle filter which is applied for human tracking on active camera. The proposed system consists of three major parts which are human detection, human tracking, and camera control. The codebook matching algorithm is used for extracting human reg...

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Main Authors: Prasad, Mukesh, Chang, Liang-Cheng, Gupta, Deepak, Pratama, Mahardhika, Sundaram, Suresh, Lin, Chin-Teng
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2019
Subjects:
Online Access:https://hdl.handle.net/10356/90082
http://hdl.handle.net/10220/49423
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-900822020-03-07T11:49:00Z Online video streaming for human tracking based on weighted resampling particle filter Prasad, Mukesh Chang, Liang-Cheng Gupta, Deepak Pratama, Mahardhika Sundaram, Suresh Lin, Chin-Teng School of Computer Science and Engineering Human Tracking Particle Filter Engineering::Computer science and engineering This paper proposes a weighted resampling method for particle filter which is applied for human tracking on active camera. The proposed system consists of three major parts which are human detection, human tracking, and camera control. The codebook matching algorithm is used for extracting human region in human detection system, and the particle filter algorithm estimates the position of the human in every input image. The proposed system in this paper selects the particles with highly weighted value in resampling, because it provides higher accurate tracking features. Moreover, a proportional–integral–derivative controller (PID controller) controls the active camera by minimizing difference between center of image and the position of object obtained from particle filter. The proposed system also converts the position difference into pan-tilt speed to drive the active camera and keep the human in the field of view (FOV) camera. The intensity of image changes overtime while tracking human therefore the proposed system uses the Gaussian mixture model (GMM) to update the human feature model. As regards, the temporal occlusion problem is solved by feature similarity and the resampling particles. Also, the particle filter estimates the position of human in every input frames, thus the active camera drives smoothly. The robustness of the accurate tracking of the proposed system can be seen in the experimental results. Published version 2019-07-18T04:26:03Z 2019-12-06T17:40:16Z 2019-07-18T04:26:03Z 2019-12-06T17:40:16Z 2018 Journal Article Prasad, M., Chang, L.-C., Gupta, D., Pratama, M., Sundaram, S., & Lin, C.-T. (2018). Online video streaming for human tracking based on weighted resampling particle filter. Procedia Computer Science, 144, 2-12. doi:10.1016/j.procs.2018.10.499 1877-0509 https://hdl.handle.net/10356/90082 http://hdl.handle.net/10220/49423 10.1016/j.procs.2018.10.499 en Procedia Computer Science © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). 11 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Human Tracking
Particle Filter
Engineering::Computer science and engineering
spellingShingle Human Tracking
Particle Filter
Engineering::Computer science and engineering
Prasad, Mukesh
Chang, Liang-Cheng
Gupta, Deepak
Pratama, Mahardhika
Sundaram, Suresh
Lin, Chin-Teng
Online video streaming for human tracking based on weighted resampling particle filter
description This paper proposes a weighted resampling method for particle filter which is applied for human tracking on active camera. The proposed system consists of three major parts which are human detection, human tracking, and camera control. The codebook matching algorithm is used for extracting human region in human detection system, and the particle filter algorithm estimates the position of the human in every input image. The proposed system in this paper selects the particles with highly weighted value in resampling, because it provides higher accurate tracking features. Moreover, a proportional–integral–derivative controller (PID controller) controls the active camera by minimizing difference between center of image and the position of object obtained from particle filter. The proposed system also converts the position difference into pan-tilt speed to drive the active camera and keep the human in the field of view (FOV) camera. The intensity of image changes overtime while tracking human therefore the proposed system uses the Gaussian mixture model (GMM) to update the human feature model. As regards, the temporal occlusion problem is solved by feature similarity and the resampling particles. Also, the particle filter estimates the position of human in every input frames, thus the active camera drives smoothly. The robustness of the accurate tracking of the proposed system can be seen in the experimental results.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Prasad, Mukesh
Chang, Liang-Cheng
Gupta, Deepak
Pratama, Mahardhika
Sundaram, Suresh
Lin, Chin-Teng
format Article
author Prasad, Mukesh
Chang, Liang-Cheng
Gupta, Deepak
Pratama, Mahardhika
Sundaram, Suresh
Lin, Chin-Teng
author_sort Prasad, Mukesh
title Online video streaming for human tracking based on weighted resampling particle filter
title_short Online video streaming for human tracking based on weighted resampling particle filter
title_full Online video streaming for human tracking based on weighted resampling particle filter
title_fullStr Online video streaming for human tracking based on weighted resampling particle filter
title_full_unstemmed Online video streaming for human tracking based on weighted resampling particle filter
title_sort online video streaming for human tracking based on weighted resampling particle filter
publishDate 2019
url https://hdl.handle.net/10356/90082
http://hdl.handle.net/10220/49423
_version_ 1681049777766137856