Real-time Traffic Light Recognition on Mobile Devices with Geometry-Based Filtering

Understanding the status of the traffic signal at an intersection is crucial to many vehicle applications. For example, the information can be utilized to estimate the optimal speed for passing the intersection to increase the fuel efficiency, or to provide additional context information for predicting whether a vehicle would run the red light. In this paper, we propose a new real-time traffic light recognition system with very low computational requirements, suitable for use in mobile devices, such as smartphones and tablets, and video event data recorders. Our system does not rely on complex image processing techniques for detection; instead, we utilize a simple geometry-based technique to eliminate most false detections. Moreover, the proposed system performs well in general and realistic conditions, i.e., vibration caused by rough roads. Evaluation of our proposed system is performed with data collected from a smartphone onboard a scooter, including video footage recorded from the camera and data collected by GPS. It is shown that our system can accurately recognize the traffic light status in real-time as a vehicle carrying the device approaching the intersection.