Real-time road traffic classification using mobile video cameras

On board video analysis has attracted a lot of interest over the two last decades with as main goal to improve safety by detecting obstacles or assisting the driver. Our study aims at providing a real-time understanding of the urban road traffic. Considering a video camera fixed on the front of a public bus, we propose a cost-effective approach to estimate the speed of the vehicles on the adjacent lanes when the bus operates on a dedicated lane. We work on I-D segments drawn in the image space, aligned with the road lanes. The relative speed of the vehicles is computed by detecting and tracking features along each of these segments. The absolute speed can be estimated from the relative speed if the camera speed is known, e.g. thanks to an odometer and/or GPS. Using pre-defined speed thresholds, the traffic can be classified into different categories such as 'fluid', 'congestion' etc. The solution offers both good performances and low computing complexity and is compatible with cheap video cameras, which allows its adoption by city traffic management authorities.