In order to enhance the performance of compressive sensing based tracking in complex scenarios

we propose an improved tracking algorithm based on a new feature selection approach and target model updating mechanism. First

we select features allowing to distinguish a target from the background

according to the distance between a feature's positive and negative class conditional probability Gaussian distributions. Second

we adaptively update the target appearance model according to the difference between the current target and the original one

so that the target would not be updated in case of big occlusion or frequent posture changes. Experiments on ten standard and complex test video sequences demonstrated that for the three measurements

i.e. center error

the success rate

and the precision plot

our algorithm

with the rate of 85.19% of frames correctly tracked and average 13.74 pixels of center location difference

achieves a higher perform than three state-of-the-art methods. The proposed new method of feature selection and target model updating

enhances the robustness of compressive sensing based tracking and speed up of the track.