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A Fast-Multiplying PSO Algorithm for Real-Time Multiple Object Tracking
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 60 - Number 3 |
| Year of Publication: 2012 |
| Authors: Fakheredine Keyrouz |
10.5120/9669-4098
|
Fakheredine Keyrouz . A Fast-Multiplying PSO Algorithm for Real-Time Multiple Object Tracking. International Journal of Computer Applications. 60, 3 ( December 2012), 1-6. DOI=10.5120/9669-4098
Abstract
The problem of real-time object tracking in live video sequences is of increasing importance today mainly due to higher security requirements for surveillance applications. In this study we present a novel particle swarm optimization (PSO) algorithm with additional new features. The basic idea of PSO is to use one swarm or one hierarchical swarm of particles to find the best estimate or the global optimum for the object location in a given search space. Particles fly around, share information with each other, and optimize their behavior to find the global optimum. Until today, PSO was used to track one pre-classified pattern of objects. The existing algorithms apply only one swarm of particles to track predefined patterns. The algorithm we present in this paper extended the PSO algorithm to track different objects having non-predefined patterns: n swarms are used to track n objects, i. e. to find n local maxima in different parts of the search space. The proposed algorithm introduces two new components to PSO. A self-adapting component, which is robust against drastic brightness changes of the image sequence, and a self-splitting component, which decides to track the scene as one connected object, or as more stand-alone objects.
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