Traffic signs can be analyzed using forward-facing cameras in many modern cars, vehicles and trucks. One of the basic use cases of a traffic-sign recognition system is for speed limits. Most of the
GPS data would procure speed information, but additional speed limit traffic signs can also be used to extract information and display it in the dashboard of the car to alert the driver about the road sign. This is an advanced
driver-assistance feature available in most high-end cars, mainly in European vehicles. Modern traffic-sign recognition systems are being developed using convolutional neural networks, mainly driven by the requirements of autonomous vehicles and
self-driving cars. In these scenarios, the detection system needs to identify a variety of traffic signs and not just speed limits. This is where the
Vienna Convention on Road Signs and Signals comes to help. A convolutional neural network can be trained to take in these predefined traffic signs and 'learn' using
Deep Learning techniques. The neural net in turn uses
Image Processing and
Computer Vision to train the network with its potential outcomes. The trained neural net can then be used in real time to detect new traffic signs in real time. Self driving car companies including
Waymo and
Uber are generating and outsourcing traffic-sign data sets along with map and navigation companies like
Tom Tom. Advanced
computer vision and neural network techniques make this goal highly efficient and achievable in real time. There are diverse algorithms for traffic-sign recognition. Common ones are those based on the shape of the sign board. Typical sign board shapes like hexagons, circles, and rectangles define different types of signs, which can be used for classification. Other major algorithms for character recognition includes
Haar-like features, Freeman
Chain code, AdaBoost detection and
deep learning neural networks methods. Haar-like features can be used to create cascaded classifiers which can then help detect the sign board characters. Deep learning can be incorporated into traffic-sign detection. Polygonal approximation of digital curves using
Ramer–Douglas–Peucker algorithm can be used to detect the shape of the sign boards and methods like
Support Vector Machines and Byte-MCT with an
AdaBoost classifier has been used in one of the methods to detect traffic signs. Identification of speed limit signs also needs to take into account the units used in a particular area. For instance, a vehicle travelling from
Northern Ireland to
Ireland would need to be able to differentiate the km/h speed limit signage in Ireland from the mph speed limit signage still used in Northern Ireland, which is particularly important when traffic sign recognition is linked to
Intelligent speed assistance systems.
Geofencing and reference to online navigation
databases can be used as a hint to the
algorithm to identify which units are likely to be in use. ==Usage==