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Social force model aided robust particle PHD filter for multiple human tracking

Feng, Pengming, Wang, Wenwu, Naqvi, Syed Mohsen, Dlay, Satnam and Chambers, Jonathon A. (2016) Social force model aided robust particle PHD filter for multiple human tracking In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 20 - 25 March 2016, Shanghai, China.

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Abstract

In this paper, we propose a novel robust multiple human tracking approach based upon processing a video signal by utilizing a social force model to enhance the particle probability hypothesis density (PHD) filter. In traditional dynamic models, the states of targets are only predicted by their own history; however, in multiple human tracking, the information from interaction between targets and the intentions of each target can be employed to obtain more robust prediction. Furthermore, such information can mitigate the problems of collision and occlusion. The cardinality of variable number of targets can also be estimated by using the PHD filter, hence improving the overall accuracy of the multiple human tracker. In this work, a background subtraction step has also been employed to identify the new born targets and provide the measurement set for the PHD filter. To evaluate tracking performance, sequences from both the CAVIAR and PETS2009 datasets are employed for evaluation, which shows clear improvement of the proposed method over the conventional particle PHD filter.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Feng, Pengming
Wang, WenwuW.Wang@surrey.ac.uk
Naqvi, Syed Mohsen
Dlay, Satnam
Chambers, Jonathon A.
Date : 19 May 2016
DOI : 10.1109/ICASSP.2016.7472508
Uncontrolled Keywords : Social force model, PHD filter, multiple human tracking
Depositing User : Melanie Hughes
Date Deposited : 20 Nov 2018 12:40
Last Modified : 20 Nov 2018 12:40
URI: http://epubs.surrey.ac.uk/id/eprint/849901

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