Title :
Real time face tracking by genetic particle filter
Author :
Liu Yanli ; Zhang Heng
Author_Institution :
Sch. of Inf. Eng., East China Jiaotong Univ., Nanchang, China
Abstract :
There are a great variety of human faces tracking methods based on particle filter. However, most tracking algorithms, so far, are unable to meet the demands for both precise and fast tracking. A real-time algorithm, based on genetic particle filter (GPF) for human faces tracking is presented in this paper. The crossover and mutation operations in evolutionary computation are introduced into PF to make samples move towards regions with large value of posterior density function (PDF). Experiments results show that GPF presents improvements over the PF techniques regarding to robustness, accuracy and flexibility in dynamic environment. Meanwhile, GPF, which needs fewer samples, improve the speed of tracking.
Keywords :
evolutionary computation; image motion analysis; particle filtering (numerical methods); target tracking; crossover operation; evolutionary computation; genetic particle filter; human face tracking method; mutation operation; posterior density functionl; real time face tracking; robustness; Genetics; Particle filters; Particle tracking; Face Tracking; Genetic Algorithm; Particle Filter; Posterior Density;
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
DOI :
10.1109/CCDC.2009.5192407