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Aiming at the problem that the single feature based tracking algorithm has weak?adaptability?in the complex scene, a improved kernelized correlation object tracking algorithm based on integral channel feature is proposed, which uses the integral channel feature with rich and diverse feature information and efficient calculation efficiency. These features are integrated into the kernelized correlation model, which can overcome the shortcomings of the single channel feature for describing the object area. In addition, an adaptive learning factor strategy is proposed, which enhances the generalization ability of the model. A large number of qualitative and quantitative experiments show that our proposed algorithm has more robust performance and anti-interference ability than the traditional KCF algorithm, which is suitable for engineering application.