This study proposes an improved high-resolution neural network to address the issue of detection and tracking failures caused by target blockage in a multi-target pedestrian tracking process. First， to enhance the initial feature extraction capability of the network for pedestrian targets， a second-generation bottleneck residual block structure was introduced into the backbone of a high-resolution neural network， thus improving the receptive field and feature expression capability. Second， a new residual detection block architecture with a two-layer efficient channel attention module was designed to replace the one at the multi-scale information exchange stage of the original network， thus improving the test performance of the entire network system. Finally， the network was fully trained by selecting appropriate parameters， and subsequently， the algorithm was tested using multiple test sets. The test results indicated that the tracking accuracy of the proposed algorithm was 0.1%， 1.6%， and 0.8% higher than that of FairMOT on 2DMOT15， MOT17， and MOT20 datasets， respectively. In conclusion， the proposed algorithm-tracking stability for longer video sequences was greatly improved. Therefore， it can be applied to special scenarios with more targets and occlusion area.