Currently, detection-based multi-target tracking methods have achieved better results in complex scene problems, but most of the existing research focuses on spatio-temporal feature correlation and neglects the global tracking benefit that can be brought by improving detection performance. Accordingly, this paper proposes a convolutional hybrid attention mechanism, which combines dynamic sparse channel attention and spatial location attention: when dealing with channel attention, it integrates spatial context information to dynamically adjust the channel weights; when dealing with spatial attention, it combines different channel features to evaluate the importance of spatial regions, aiming at optimising the allocation of attention and improving the detection accuracy. Further, this paper proposes a two-phase multi-target tracking method, CHAMTrack, to enhance the algorithm's ability to capture key information in complex scenes, improve the tracking effect of targets at different scales, and reduce the occurrence rate of omission and ID switching in the tracking process by using the attention mechanism in the detection phase of moving targets. The incidence of missed detection and ID switching during the tracking process is reduced. The experimental results on MOT17 and MOT20 datasets show that the CHAMTrack improves the MOTA metrics by $2.1\,\% $ and $ 1.3\,\%$, and the IDSw. metrics by $28\,\% $ and $20.5\,\% $, which significantly improves the effectiveness and robustness of the multi-target tracking algorithms in complex scenes.