The online multiobject tracking (MOT), which integrates object detection and tracking into a single network, has made breakthroughs in recent years. However, most online trackers have a more monotonous prediction of the tracking offset in two consecutive frames, which may not be reliable when facing extreme situations such as occlusion and object deformation. Once the tracking offset of an object is biased, its corresponding tracklet will no longer maintain temporal consistency, which will seriously affect the tracking performance. In this paper, we propose a new online multiple object tracker with feature enhancement mechanism, namely En-Tracker. In En-Tracker, a multibranch kinematic analysis network (MKANet) is designed to address the above problems. MKANet estimates the pixel offset and instance offset of the object in parallel based on imitating the human thinking to joint position and appearance representations. Note that these two types of offsets compensate and facilitate each other to effectively deal with some extreme scenarios. In addition, we propose a kinematic-assisted feature synthesis enhancement (KFSE) module, which has a more comprehensive enhancement mechanism. Specifically, KFSE propagates previous tracking information to the current frame based on kinematic trend analysis while enhancing the characterization of detection features and appearance embeddings, which not only assists in object detection but also ensures the uniqueness of the appearance embeddings. Extensive experiments on MOT16 and MOT17 verify the effectiveness and advantage of our model.