Traditional visual tracking methods only involve the characteristic information extracted from the targets

and thus

the dense context of the targets are ignored. When extracting target feature information is difficult

tracking failure can easily result. To solve the aforementioned problem

an anti-occlusion visual tracking algorithm is presented based on spatio-temporal context learning. First

the spatio-temporal relationship between the target and local contexts is used to understand the spatio-temporal context model. Second

the confidence map is calculated using the prior context model and the learned spatio-temporal context model. Finally

the spatio-temporal context region is divided into blocks to consider occlusion discrimination. If the probability of occlusion is less than the set threshold

then the object location will be determined by maximizing the probability of the new confidence map. If the probability of occlusion exceeds the set threshold

then the target will be occluded. Object location and motion trajectory are estimated by matching sub-blocks and by filtering particles to realize anti-occlusion tracking in different levels. Conducting experiments in the benchmark data set demonstrates that the success rate of the STC-PF (Spatio-Temporal Context-Particle Filter)tracking method increases to over 80%. The center error is less than those of STC algorithms. With regard to the precondition of improving anti-occlusion capacity

the running speed of the STC-PF algorithm is higher than those of current popular algorithms and has minimal difference with that of the STC algorithm. The STC-PF algorithm can be applied to visual target tracking in complex conditions

such as illumination changes

object rotation

occlusion

and so on

with a certain instantaneity and efficiency. In particular

it exhibits good anti-occlusion capability and fast running speed in the case of a partially occluded target.