In order to detect and track road targets in complex urban intersection environments, a multi-target detection and tracking algorithm based on roadside LiDAR is proposed. Firstly, the background subtraction method is used to filter out the background point cloud. Then, the curved-voxel clustering algorithm is used to detect the target to obtain 3D bounding box information with fusing 5 frame point clouds. Subsequently, a double-validation gate and life cycle management strategy with adaptive threshold are put forward, which effectively improves the accuracy of object matching and reduces object missing and false detection. Finally, the fusion algorithm of Interacting Multiple Model-Unscented Kalman Filter and Joint Probability Data Association was used to track road targets. The experimental results show that the algorithm meets the real-time requirements while ensuring detection and tracking performance, and has an engineering application value.