CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and recognizing actions of multiple hockey players using the boosted particle filter
Image and Vision Computing
Computers & Mathematics with Applications
Structural similarity-based object tracking in multimodality surveillance videos
Machine Vision and Applications
Color image segmentation using adaptive mean shift and statistical model-based methods
Computers & Mathematics with Applications
Accurate appearance-based Bayesian tracking for maneuvering targets
Computer Vision and Image Understanding
Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occlusion reasoning for tracking multiple people
IEEE Transactions on Circuits and Systems for Video Technology
A new evolutionary particle filter for the prevention of sample impoverishment
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Multimedia - Special issue on integration of context and content
Harmony filter: A robust visual tracking system using the improved harmony search algorithm
Image and Vision Computing
A parallel histogram-based particle filter for object tracking on SIMD-based smart cameras
Computer Vision and Image Understanding
Pedestrian detection and tracking in an urban environment using a multilayer laser scanner
IEEE Transactions on Intelligent Transportation Systems
A two-stage dynamic model for visual tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Fast object tracking using adaptive block matching
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Multitarget Visual Tracking Based Effective Surveillance With Cooperation of Multiple Active Cameras
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance
IEEE Transactions on Image Processing
Object Tracking in Structured Environments for Video Surveillance Applications
IEEE Transactions on Circuits and Systems for Video Technology
Monocular vision based 6D object localization for service robot's intelligent grasping
Computers & Mathematics with Applications
Stochastic volatility modeling with computational intelligence particle filters
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Ant Colony Estimator: An intelligent particle filter based on ACOR
Engineering Applications of Artificial Intelligence
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Particle filter algorithm is widely used for target tracking using video sequences, which is of great importance for intelligent surveillance applications. However, there is still much room for improvement, e.g. the so-called ''sample impoverishment''. It is brought by re-sampling which aims to avoid particle degradation, and thus becomes the inherent shortcoming of the particle filter. In order to solve the problem of sample impoverishment, increase the number of meaningful particles and ensure the diversity of the particle set, an evolutionary particle filter with the immune genetic algorithm (IGA) for target tracking is proposed by adding IGA in front of the re-sampling process to increase particle diversity. Particles are regarded as the antibodies of the immune system, and the state of target being tracked is regarded as the external invading antigen. With the crossover and mutation process, the immune system produces a large number of new antibodies (particles), and thus the new particles can better approximate the true state by exploiting new areas. Regulatory mechanisms of antibodies, such as promotion and suppression, ensure the diversity of the particle set. In the proposed algorithm, the particle set optimized by IGA can better express the true state of the target, and the number of meaningful particles can be increased significantly. The effectiveness and robustness of the proposed particle filter are verified by target tracking experiments. Simulation results show that the proposed particle filter is better than the standard one in particle diversity and efficiency. The proposed algorithm can easily be extended to multiple objects tracking problems with occlusions.