This paper will demonstrate a solution for combining applied vehicle load to measured displacement of a bridge structure. Vehicle type was determined using an advanced deep learning algorithm to locate and classify vehicles based on video footage. Ranges for vehicle weight can then be calculated using weight databases. The measurement of displacement was accomplished using a synchronised multi-camera contactless vision based multiple point displacement measurement system using wireless action cameras. Displacement measurements can provide a valuable insight into the structural condition and service behaviour of bridges under live loading. Computer Vision systems have been validated as a means of displacement calculation, however existing systems in use are limited in scope by their inability to reliably track multiple points on a long span bridge structure. The system introduced in this paper provides a low cost durable alternative which is rapidly deployable in the field. The performance of the system was evaluated in a series of controlled laboratory tests. This research provides a means of correlating applied load to measured displacement, allowing for low cost condition rating of bridge structures.