An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking

A new moving object tracking method using particle filter and probability product kernel

“…In [9], the authors proposed a hybrid valued sequential state estimation algorithm, and its particle filter based implementation, that extends the standard color particle filter in two ways: the algorithm handled the non-rigid deformation of targets, partial occlusions and cluttered background, the main drawback of this approach is that the number of particles increases with the number of objects (i.e., the size of the state vector). In [10], the proposed algorithm has particle set optimized by immune genetic algorithm (IGA) can better express the true state of the target, and the number of meaningful particles can be increased significantly and also it is better in particle diversity, the error of state estimation, error tracking of video, the number of meaningful particles and efficiency. In [11], the main advantage in this method is too importance sample on the motion states while approximating importance sampling by posterior mode tracking for estimating illumination.…”

Ant Colony Estimator: An intelligent particle filter based on

“…Genetic Algorithm (GA) (Higuchi, 1997;Kwok et al, 2005;Pernkopf, 2008;Park et al, 2009) Evolution Strategy (ES) (Uosaki et al, 2003(Uosaki et al, , 2004Uosaki and Hatanaka, 2007), Particle Swarm Optimization (PSO) (Tong et al, 2006;Wang et al, 2006;Zheng and Meng, 2008), Artificial Fish Swarm (AFS) (Xiaolong et al, 2008), Adaptive Evolutionary Algorithm (Duan et al, 2007;Duan and Cai, 2008), Ant Colony Optimization (ACO) (Xu et al, 2009;Zhu et al, 2010), Markov chain Monte Carlo (MCMC) PSO (Jing and Vadakkepat, 2010), Immune Genetic Algorithm (IGA) (Han et al, 2011), and Invasive Weed Optimization (IWO) (Ahmadi et al, 2012).…”

Online multi-person tracking via robust collaborative model

“…For instance, Rui and Chen [15] have used the unscented Kalman Filter (UKF) for generating the proposal distribution of the particle filter, and the resulting scheme is known as the unscented particle filter (UPF). An immune genetic algorithm for visual tracking was subsequently introduced [17]. Recently, in [6] the Metropolis Hastings algorithm has been used to sample particles from associated detections in the trackingby-detection framework.…”

“…Recently, in [6] the Metropolis Hastings algorithm has been used to sample particles from associated detections in the trackingby-detection framework. However, these methods do not exploit the collaboration between detectors and trackers [15,17], or consider the effect of false positive detections on the trackers [6].…”