This thesis presents a novel self-powered infrastructural traffic monitoring approach that estimates traffic information by combining three detection techniques. The traffic information can be obtained from the presented approach includes vehicle counts, speed estimation and vehicle classification based on size. Two categories of sensors are used including IR Lidar and IR camera. With the two sensors, three detection techniques are used: Time of Flight (ToF) based, vision based and Laser spot flow based. Each technique outputs observations about vehicle location at different time step. By fusing the three observations in the framework of Kalman filter, vehicle location is estimated, based on which other concerned traffic information including vehicle counts, speed and class is obtained. In this process, high reliability is achieved by combing the strength of each techniques. To achieve self-powering, a dynamic power management strategy is developed to reduce system total energy cost and optimize power supply in traffic monitoring based on traffic pattern recognition. The power manager attempts to adjust the power supply by reconfiguring system setup according to its estimation about current traffic condition. A system prototype has been built and multiple field experiments and simulations were conducted to demonstrate traffic monitoring accuracy and power reduction efficacy. / Master of Science