Human tracking in video surveillance using particle filter

Yussiff, A.-L. and Yong, S.-P. and Baharudin, B.B. (2016) Human tracking in video surveillance using particle filter. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Automated human tracking is a task that has a wide area of applications and has become more important nowadays. This research proposes to investigate the use of Bayesian inference technique specifically particle filter for tracking human in video surveillance. Kalman filter which has been the de facto technique for real world tracking performs poorly for most of the problems because, the real world applications are often non-linear and non Gaussian. The particle filter on the other hand is a tool for estimating the posterior probability density of state of a dynamic model that includes non-linear and non-Gaussian real world applications. The filter uses random sample to estimate the possible location of the tracked object in the next immediate frame even in the presence of occlusion. In order to initialize the tracking process, humans are first detected using a pretrained human detection model in video. The detector utilize model fusing method which is the combination of histogram of oriented gradient based human detector model and Haar feature based upper body detector to locate position of moving person in video. The technique performed excellently well when evaluated on the publicly available CAVIAR dataset and outperformed the Kalman filter algorithm. © 2015 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 2; Conference of 2015 International Symposium on Mathematical Sciences and Computing Research, iSMSC 2015 ; Conference Date: 19 May 2015 Through 20 May 2015; Conference Code:124374
Uncontrolled Keywords: Bandpass filters; Bayesian networks; Estimation; Gaussian noise (electronic); Inference engines; Kalman filters; Monitoring; Monte Carlo methods; Probability density function, Human Tracking; Object Tracking; Particle filter; Probabilistic inference; Surveillance video, Security systems
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:18
Last Modified: 09 Nov 2023 16:18
URI: https://khub.utp.edu.my/scholars/id/eprint/6744

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