Existing Siamese-based tracking algorithms usually utilize local features to represent the object, which lack sufficient discrimination and may degrade tracking performance in challenging situations. To address this issue, we propose a novel object-aware network to improve feature representation and achieve robust object tracking. The proposed object-aware network contains a background filter module (BFM), channel complementary module (CCM), and template adaptive network (TAN). Specifically, by locating the target in the initial frame on the feature maps, BFM suppresses the background interference of the target template. CCM captures the global context by exploring the complementary information of each channel. The lightweight TAN adaptively recognizes valuable features for the target and represents the target template just through a single vector. Benefiting from these three components, the object-aware network enhances the discrimination of feature maps and alleviates background interference to some extent. The proposed object-aware network could be integrated with the Siamese-based backbone network for real-time object tracking, named SiamOAN. Extensive experiments on the six challenging benchmarks including OTB100, UAV123, VOT2016, VOT2018, GOT10k, and LaSOT, show that the proposed SiamOAN outperforms many state-of-the-art trackers and runs at approximately 67 fps on GPU RTX3090.