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#$NL: With the development of video processing and network technology, video surveillance applications gradually penetrated into every aspect of people"s daily activities.How to design a object tracking technique with high precision and robustness is still a hotspot and difficulty in current research. This paper proposes an improved spatio-temporal contexttracking algorithm based on multi-feature fusion and adaptive model updating. Based on the spatio-temporacontext tracking algorithm, our proposed algorithm integrates multi-feature informations into the spatio-temporacontextmodel. Since the complementary characteristics of multiple features, it is possible to overcome the disadvantages of the single feature and improve the anti-jamming ability. In addition, this paper also proposes an adaptive learning factor strategy to enhance the generalization ability of the model. A large number of simulation results show that the tracking performance of our proposed algorithm outperforms the traditional spatio-tempora contexttracking algorithm, and has stronger robustness and anti-jamming capability for complex scenes.#$NLKeywords:Object tracking; Multi-feature fusion; Adaptive; Spatio-tempora; Generalization ability; Complementary property