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Title: Online video streaming for human tracking based on weighted resampling particle filter
Authors: Prasad, Mukesh
Chang, Liang-Cheng
Gupta, Deepak
Pratama, Mahardhika
Sundaram, Suresh
Lin, Chin-Teng
Keywords: Human Tracking
Particle Filter
Engineering::Computer science and engineering
Issue Date: 2018
Source: 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
Series/Report no.: Procedia Computer Science
Abstract: 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.
ISSN: 1877-0509
Rights: © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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