This paper presents a novel infrastructural traffic monitoring approach that estimates traffic information by combining two sensing techniques. The traffic information can be obtained from the presented approach includes passing vehicle counts, corresponding speed estimation and vehicle classification based on size. This approach uses measurement from an array of Lidars and video frames from a camera and derives traffic information using two techniques. The first technique detects passing vehicles by using Lidars to constantly measure the distance from laser transmitter to the target road surface. When a vehicle or other objects pass by, the measurement of the distance to road surface reduces in each targeting spot, and triggers detection event. The second technique utilizes video frames from camera and performs background subtraction algorithm in each selected Region of Interest (ROI), which also triggers detection when vehicle travels through each ROI. Based on detection events, vehicle location is estimated respectively. The final location estimation is derived by fusing the two estimation in the framework of Recursive Bayesian Estimation (RBE). Vehicle counts, speed estimation and classification are then performed using the vehicle location estimation in each time step. The approach achieves high reliability by combing the strength of both sensors. A sensor prototype has been built and multiple field experiments have been completed. High reliability is demonstrated in experiment by achieving more than 95% accuracy both in vehicle counting and classification.