Aiming at problems that fast compressive tracking（ FCT）

algorithm has poor robustness in target occlusion and illumination changes

propose a FCT algorithm with context based on image sensors. First

the new algorithm introduces temporal and spatial context features in the Haar-like feature

and assists to estimate target position by spatial information around target and time recurrence relation. By improved random measurement matrix

extract simultaneously texture features and gray features of target

stability of feature is enhanced

accuracy of target tracking is improved. Pre-judge candidate sample by variance classifier

reduce number of decision

reduce number of wrong candidate samples. The improved FCT algorithm has good invariance for illumination

rotation and scale changes

tracking drift also not easy to happen. It can be proved that the improved FCT algorithm is superior to CT and FCT algorithms.