Pedestrian multiple object tracking targets to track multiple pedestrian instances in real-time. Recently, the methods based on joint detection and embedding have improved performance by sharing task features. However, it has two obvious shortcomings: inconsistent task information and ambiguous neighbor instance overlap. Hence, the branch tasks information gap and instances information gap need to be carefully addressed. In this paper, IGTracker is proposed as a novel online tracking framework, which bridges different branch task optimization requirements from the perspective of task-specific information gaps and nearest instance information gaps. Firstly, to alleviate the competitive conflict between subtasks, we propose a shuffle involution decoupling (SID) module, which constructs task-specific features by focusing on local interaction information and global long-range dependencies of key points. Secondly, the nearest neighbor information enhancement (NNIE) strategy is proposed to reduce the ambiguity between similar instances by leveraging the adjacency key point information gap. As a bonus, our proposed IGTracker achieves competitive performance compared to various existing methods on the MOTChallenge benchmarks.