Particle filter algorithm based on posterior probability measurement

To meet visual tracking requirements in complex environment where obscuration, illumination change and size variation may occur, this paper presents a particle filter algorithm based on posterior probability measurement. Compared with Bhattacharyya coefficient similarity measurement index, the posterior probability measurement index has stronger peak value characteristic. This paper uses the posterior probability index as similarity measurement function, and realizes the tracking algorithm by particle update, propagation, observation and estimation. The video image sequences were tested for object tracking. The experimental results show that only 50% of the image sequences can be scale-adaptive by the traditional algorithm, while this algorithm can converge to the real contrail of object by 25% particles of the traditional algorithm, the obscuration resistant capability is improved, and 90% of image sequences can get scale-adaptive effect.