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Social Force Model based MCMC-OCSVM Particle PHD Filter for Multiple Human Tracking

Wang, W, Feng, P, Dlay, S, Naqvi, SM and Chambers, J (2017) Social Force Model based MCMC-OCSVM Particle PHD Filter for Multiple Human Tracking IEEE Transactions on Multimedia.

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Abstract

Video based multiple human tracking often involves several challenges including target number variation, object occlusions, and noise corruption in sensor measurements. In this paper, we propose a novel method to address these challenges based on probability hypothesis density (PHD) filtering with a Markov chain Monte Carlo (MCMC) implementation. More specifically, a novel social force model (SFM) for describing the interaction between the targets is used to calculate the likelihood within the MCMC resampling step in the prediction step of the PHD filter, and a one class support vector machine (OCSVM) is then used in the update step to mitigate the noise in the measurements, where the SVM is trained with features from both colour and oriented gradient histograms. The proposed method is evaluated and compared with state-of-the-art techniques using sequences from the CAVIAR, TUD and PETS2009 datasets based on the mean Euclidean tracking error on each frame, the optimal subpattern assignment (OSPA) metric, and the multiple object tracking precision (MOTP) metric. The results show improved performance of the proposed method over the baseline algorithms including the traditional particle PHD filtering method, the traditional SFM based particle filtering method, multi-Bernoulli filtering and an online-learning based tracking method.

Item Type: Article
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Wang, WUNSPECIFIEDUNSPECIFIED
Feng, PUNSPECIFIEDUNSPECIFIED
Dlay, SUNSPECIFIEDUNSPECIFIED
Naqvi, SMUNSPECIFIEDUNSPECIFIED
Chambers, JUNSPECIFIEDUNSPECIFIED
Date : 2017
Copyright Disclaimer : (c) 2016. IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 06 Dec 2016 15:09
Last Modified : 06 Dec 2016 15:09
URI: http://epubs.surrey.ac.uk/id/eprint/813052

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