This week, I am handling several things at the same time to keep developing the algorithm and while searching over related works.
First, traffic sign recognition user interface namded Traffic Sign Recognizer (TSR) is under development since we now have the trainned ANN and need a interface to test its performance. The new GUI for traffic sign recognition is being built on top of the polygon detection algorithm that I introduced at Traffic sign feature extraction. Evidently, my polygon detection algorithm is not the perfect solution so I am adding the manual region selection function. You can see the screen shot showing the manual region assigning procedure to extract the ROI from the real scene image: Develop traffic sign recognition modules
For following weeks, TSR will be extended to have several additional features:
Besides, last week I used FANN, which is the ANN engine we are currently using, as the stand-alone application and so did not realize the big problem that I found this week. Their main library is developed in C language. So we need a function exporting definition header file to import their libraries into our application. But there were several problems in their header definition and also in a number of source codes to be compiled together with other MFC applications. So it took some time to modify their source codes and finally it is working very well. So now we can compile the FANN library with any program developed in Visual C++ 6.0. You can download the fixed FANN engine (~12 MB) here: Selecting a proper ANN module#FANN bug fix
I did some survey on ANN training that Prof. Tsai has been concerned. You may remember that I once introduced about the adaptive training that add nodes and layers incrementally for the training. I did some survey and found that technology is now called as cascade training. I found some reference and will read and apply their technology in following weeks. I put a short introduction on cascaded training here: Extension for the traffic sign recognition#Adpative training to determine the proper number of layers and nodes
Finally, I read the Liu and Ran's paper on the stop sign recognition that Prof. Tsai sent me. As summary, they detected the stop sign from the given image by using the color segmentation on HSV color space and after check the object width, aspect ratio and symmetrical level to filter out the stop sign region candidates. This extract ROI image after resizing to 30x30 pixels is directly put into the ANN configured 30x30 (900) input nodes, 1 hidden layer with 6 hidden neurons, and 2 output neurons indicating "stop sign" or "not". Their training curve shows the typical back propagation curvature showing the convergence. Their over whole experiment is simple and not creative compared to other works.
So I am looking and reading more references and collecting those at here: Traffic sign recognition papers
I will post reviews of each paper to Reviews on TSPR related projects.