Multi-Target Multi-Camera Tracking （MTMCT） holds significant application values in fields such as intelligent surveillance and intelligent transportation. However， due to differences in visual angles， lighting， and imaging quality of different cameras， there are high intra-target variability and inter-target similarity， posing severe challenges to cross-camera multi-target tracking. Although the existing offline methods have made progress， the limitations such as high latency and complex post-processing operations make them unsuitable for real-world scenarios requiring online processing. Therefore， an online cross-camera multi-target tracking algorithm combining DINOv2 （Data-efficient image network version 2） and ByteTracker was proposed. First， during feature extraction， DINOv2 was employed to design and train an appearance feature extraction network， thereby obtaining more robust and discriminative target appearance features. Then， based on the ByteTrack algorithm， by optimizing the data association strategy and trajectory management mechanism， the ByteTracker algorithm was proposed to generate candidate trajectories for each camera. Finally， through the Inter-Camera Association （ICA） module， hierarchical clustering was employed to associate these candidate trajectories across different cameras， thereby linking the targets to the global identities. Validation results on the CityFlowV2 dataset’s Scene 02 （S02） show that the IDentification F1 score （IDF1）， IDentification Precision （IDP）， and IDentification Recall （IDR） metrics of proposed algorithm are 81.24%， 84.59%， and 78.14%， respectively， which are improved by 2.84， 5.79， and 0.14 percentage points， respectively compared to those of the baseline model Shim. Moreover， the tracking time of the proposed algorithm under multi-camera online conditions is only 0.14 seconds.