Multi-Object Multi-Camera Tracking is a key technology in building intelligent traffic monitoring systems. It is mainly used to detect and track specific objects and associate them in cross-camera. However, due to the heavy occlusions and rapid change of light in the real world, the tracking performance often drops significantly, making this a challenging task. In this technology, to overcome the aforementioned challenges, principal component analysis (PCA) and independent component analysis (ICA) were both utilized for post-processing to reduce the noise effect on object appearance features. Finally, a gain-and-suppression score method was proposed to improve the correctness of the cross-camera object association. Compared with other technologies, it can significantly reduce the number of model parameters by about 85% and achieve 2 times the effect of inference acceleration while maintaining the same accuracy. It can be further applied in areas such as intelligent transportation and surveillance security, providing advanced and efficient solutions.