Hi, I am Jason,I am studying the traffic sign detection, would you mind send me the matlab code?
Thank you!

Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or
prohibit certain actions. Fast real-time and robust automatic traffic sign detection and recognition
can support and disburden the driver and significantly increase driving safety and comfort.
Automatic recognition of traffic signs is also important for an automated intelligent driving vehicle
or for driver assistance systems. This paper presents a study to recognize traffic sign patterns using
Neural Network technique. Images are pre-processed with several image processing techniques,
such as, threshold techniques, Gaussian filter, Canny edge detection, Contour and Fit Ellipse.
Then, the Neural Networks stages are performed to recognize the traffic sign patterns. The system
is trained and validated to find the best network architecture. The experimental results show highly
accurate classifications of traffic sign patterns with complex background images as well as the
results accomplish in reducing the computational cost of this proposed method.

In traffic environments, Traffic Sign Recognition (TSR) is used to regulate
traffic signs, warn drivers, and command or prohibit certain actions. Fast real-time and robust automatic traffic sign detection and recognition can support and
disburden the driver, and thus, significantly increase driving safety and comfort.
Generally, traffic signs provide the driver with a variety of information for safe
and efficient navigation. Automatic recognition of traffic signs is, therefore,
important for automated intelligent driving vehicle or for driver assistance system.
However, identification of traffic signs with respect to various natural background
viewing conditions still remains a challenging task. Traffic Sign Recognition
Systems usually have been developed into two specific phases [1-7]. The first
phase is normally related to the detection of traffic signs in a video sequence or an
image using image processing. The second one is related to recognition of those
detected signs, which deals with the interest of performance in an artificial neural
network. The detection algorithms are normally based on shape or color
segmentation. The segmented potential regions are extracted as input in
recognition stage. The efficiency and speed of the detection play important roles
in the system. To recognize traffic signs, various methods for automatic traffic
sign identification have been developed and shown promising results. Neural
Networks precisely represents a technology used in traffic sign recognition [1-8].
One specific area in which many neural network applications have been
developed is the automatic recognition of signs.