As the number of motor vehicles continues to increase the problem of traffic congestion in cities becomes more and more apparent, an intelligent traffic system is designed in this paper to alleviate traffic congestion. In the vehicle detection section: The problem of ghosting from ViBe background modelling is solved using background mean modelling, and the adaptive background update strategy is implemented by assigning different background update rates to backgrounds of different complexity. Traffic light timing section: To address the problem of single-stage fuzzy control with queue length as the fuzzy control input error, a two-stage fuzzy controller based on congestion intensity is constructed, and the timing scheme is derived after fuzzy inference and clarification, so as to make adjustments to the green light time. The experimental results show that in the traffic flow detection part: Through the testing of different types of traffic scenarios, the comprehensive accuracy of the improved ViBe algorithm in vehicle flow is improved by 11% compared to the ViBe algorithm, which can provide accurate data support for the timing strategy. In the traffic light timing part: Compared with the existing traffic light timing methods, the signal light timing strategy based on two-level fuzzy control proposed in this paper reduces the average vehicle delay time and the average vehicle travel time by more than 3.34 and 5.65 s respectively under three traffic scenarios, which can play a role in easing traffic congestion.