Tracking pedestrians with bacterial foraging optimization swarms

Author

Nguyen, Hoang Thanh ; Bhanu, Bir

Author_Institution

Center for Res. in Intell. Syst., Univ. of California, Riverside, CA, USA

fYear

2011

fDate

5-8 June 2011

Firstpage

491

Lastpage

495

Abstract

Pedestrian tracking is an important problem with many practical applications in fields such as security, animation, and human computer interaction (HCI). In this paper, we introduce a previously-unexplored swarm intelligence approach to multi-object monocular tracking by using Bacterial Foraging Optimization (BFO) swarms to drive a novel part-based pedestrian appearance tracker. We show that tracking a pedestrian by segmenting the body into parts outperforms popular blob based methods and that using BFO can improve performance over traditional Particle Swarm Optimization and Particle Filter methods.

Keywords

image segmentation; object tracking; particle filtering (numerical methods); particle swarm optimisation; bacterial foraging optimization swarms; blob-based methods; human computer interaction; multiobject monocular tracking; part-based pedestrian appearance tracker; particle filter methods; pedestrian tracking; swarm intelligence approach; Histograms; Image color analysis; Microorganisms; Optimization; Particle swarm optimization; Target tracking; bacterial foraging optimization; monocular pedestrian tracking; swarm intelligence; uncalibrated cameras;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2011 IEEE Congress on

Conference_Location

New Orleans, LA

ISSN

Pending

Print_ISBN

978-1-4244-7834-7

Type

conf

DOI

10.1109/CEC.2011.5949658

Filename

5949658