3D multi-object tracking technology can help unmanned vehicles accurately perceive their surroundings, identify objects such as pedestrians and vehicles, and predict their movement trajectories, which is important for improving the safety of unmanned systems and reducing traffic accidents. In order to improve the performance of 3D multi-object tracking, a detection-based 3D multi-object tracking framework is proposed, which generates a 3D bounding box with identity, position, and shape in real time from the point cloud provided in each successive frame. Specifically and firstly, to address the shortcomings of traditional IoU, an efficient adaptive conversion intersection and merger ratio (AC-IoU) method is proposed to optimize the data metric between prediction and actual detection. Secondly, in order to increase the number of successful matches and enhance the accuracy of the trajectory, a cascade matching method combining spatial correlation and geometric features is proposed, where the first stage is responsible for matching objects with high confidence, and the second stage focuses on handling objects that are difficult to correlate due to occlusion, low confidence, or other complex situations. Experimental results on the KITTI dataset show that the tracking accuracy is improved by 0.79%, the tracking precision is improved by 3.67%, and the frame rate has reached 115 f/s. These results prove the efficiency and reliability of this paper's method in the task of tracking objects such as pedestrians and cars.