The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier Swarms for Human Detection in Infrared Imagery
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8 - Volume 08
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Dynamic Appearance Modeling for Human Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
International Journal of Computer Vision
Robust Object Tracking Via Online Dynamic Spatial Bias Appearance Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean shift blob tracking with kernel histogram filtering and hypothesis testing
Pattern Recognition Letters
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient multi-feature PSO for fast gray level object-tracking
Applied Soft Computing
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This article presents a new face tracking algorithm that employs a swarm-intelligence based method particle swarm optimisation (PSO). Firstly, all potential solutions are projected into a high-dimensional space where particles are initialised. Then, particles are driven by PSO rules to search for the solutions. The face is tracked when the particles reach convergence. Furthermore, a multi-feature model is also proposed for face description to enhance the tracking accuracy and efficiency. The proposed model and algorithm are object-independent and can be used for any free-selected object tracking. Experimental results on face tracking demonstrate that the proposed algorithm is efficient and robust in visual object tracking under dynamic environments with real-time performance.