combining sequential and parallel tracking strategies in motion mining: new approach

In recent years, research into motion mining and tracking of moving objects in real-time have attracted the attention of many researchers. therefore, a new model for motion mining based on a combination of sequential and parallel tracking strategies has been presented in this paper in order to take advantage of them and reducing their shortcomings simultaneously. in fact, combining tracker-level model helps to choose the right motion mining algorithm based on input data features, and also reduces tracking error by synchronizing tracker activity with parallel and series strategies simultaneously. in comparison with other existing solutions, this model provides important advantages such as decreasing the response time, improving the speed and increasing accuracy for tracking moving objects in the higher layers.