Paper Info
Title
Efficient multi-feature PSO for fast gray level object-tracking.
Abstract
Robust and real-time moving object tracking is a tricky job in computer vision systems. The development of an efficient yet robust object tracker faces several obstacles, namely: dynamic appearance of deformable or articulated targets, dynamic backgrounds, variation in image intensity, and camera (ego) motion. In this paper, a novel tracking algorithm based on particle swarm optimization (PSO) method is proposed. PSO is a population-based stochastic optimization algorithm modeled after the simulation of the social behavior of bird flocks and animal hordes. In this algorithm, a multi-feature model is proposed for object detection to enhance the tracking accuracy and efficiency. The object's model is based on the gray level intensity. This model combines the effects of different object cases including zooming, scaling, rotating, etc. into a single cost function. The proposed algorithm is independent of object type and shape and can be used for many object tracking applications. Over 30 video sequences and having over 20,000 frames are used to test the developed PSO-based object tracking algorithm and compare it to classical object tracking algorithms as well as previously published PSO-based tracking algorithms. Our results demonstrate the efficiency and robustness of our developed algorithm relative to all other tested algorithms.
Year
DOI
Venue
2014
10.1016/j.asoc.2013.07.008
Appl. Soft Comput.
Keywords
Field
DocType
classical object,robust object tracker,efficient multi-feature pso,fast gray level object-tracking,object type,pso-based object tracking algorithm,population-based stochastic optimization algorithm,tracking accuracy,object tracking,proposed algorithm,object detection,different object case,swarm intelligence,particle swarm optimization
Particle swarm optimization,Object detection,Population,Computer vision,Object type,Computer science,Swarm intelligence,Zoom,Robustness (computer science),Video tracking,Artificial intelligence,Machine learning
Journal
Volume
ISSN
Citations 
14
1568-4946
9
PageRank 
References 
Authors
0.43
38
3
Name
Order
Citations
PageRank
Ahmed M. Abdel Tawab190.43
M. B. AbdelHalim2457.21
S. E. D. Habib3141.53