As the traditional interactive multiple model particle filter tracker prob ability calculation method has poor robustness in complex environment,a novel sub-tracker probability calculation method based on collaborative likelihood function is proposed.Firstly,the Bhattacharyya distance between tr acking result and immediate appearance model is measured as the instantaneous likelihood function to evalua te abrupt appearance changes, and the error exponential function between tracking result with reconstruction a ppearance model obtained from l2regularized least sequel algorithm is measured as the stable likelihood funct ion to evaluate slow changes. Then,the collaborative likelihood function based on multiple features is measur ed by weighted sum strategy. Last,tracker probability is updated by combining collaborative likelihood funct ion and prior state interacting model.Comparative results of online evaluation for tracker quality in complex e nvironment demonstrate that the collaborative likelihood function can effectivel y evaluate the changes of tracker quality caused by different interference factors,and utilizing it to calculate tracker probability can get better robustness than state-of-the-art algorithms.