In the traditional bootstrap particle filter, the state transition density is used as the importance sampling function, which brings some problems such as particle degradation and poor tracking accuracy. In this paper, the posterior probability is used as the importance sampling function and its estimation method is proposed. By means of cubature information filtering and Gating technique, the mean and variance of the importance sampling function are estimated, and the importance sampling function is designed. The improved particle filter method is used to estimate the number of targets and the number of targets in the nonlinear situation. The simulation results show that the proposed algorithm has the advantages of high estimation accuracy and good stability in the nonlinear multi-target tracking scenario.
The performances of Background Difference Method and Frame-difference Method for detecting moving objects are
analyzed. It is found that as the second method is characterized by being sensitive only to moving objects, it can be used to
update the background image, an important step for the first method. A new technique integrating both methods is then
proposed. Using the consecutive frames differencing images and the detection result of the previous frame, we restore
background model, which can be utilized later to eliminate the detection error caused by the Frame-difference Method.
Based on this truth, we design a detecting system to realize the new algorithm. Experiments on indoor and outdoor video
streams show that the new technique has strong adaptability and veracity.