Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Particle Swarms as Video Sequence Inhabitants For Object Tracking in Computer Vision
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
ACM Computing Surveys (CSUR)
An Approximation to Mean-Shift via Swarm Intelligence
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Performance evaluation of a real time video surveillance system
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Machine Vision and Applications
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
Object Tracking Using Grayscale Appearance Models and Swarm Based Particle Filter
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
A swarm-intelligence based algorithm for face tracking
International Journal of Intelligent Systems Technologies and Applications
Object Tracking via Multi-region Covariance and Particle Swarm Optimization
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Enhancing particle swarm optimization based particle filter tracker
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Robust tracking in aerial imagery based on an ego-motion Bayesian model
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Machine Vision Beyond Visible Spectrum
Machine Vision Beyond Visible Spectrum
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
An approach to multimodal biomedical image registration utilizing particle swarm optimization
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
IEEE Transactions on Image Processing
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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.