07-05-2013, 03:56 PM
Computational Intelligence in Traffic Sign Recognition
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Executive summary
Traffic sign detection and recognition is a field of applied computer vision research concerned with the automatic detection and classification or recognition of traffic signs in scene images acquired from a moving car. Driving is a task based fully on visual information processing. The traffic signs define a visual language interpreted by drivers. Traffic signs carry many information necessary for successful driving; they describe current traffic situation, define right-of-way, prohibit or permit certain directions, warn about risky factors etcetera. Traffic signs also help drivers with navigation, and besides that they occur in standardized positions in traffic scenes, their shapes, colours and pictograms are known (because of international standards). To see the problem in its whole complexness we must add additional features that influence the recognition system design and performance. Traffic signs are acquired from car moving on the (often uneven) road surface by considerable speed. The traffic scene images then often suffer from vibrations; colour information is affected by varying illumination. Traffic signs are frequently occluded partially by other vehicles. Many objects are present in traffic scenes which make the sign detection hard. Furthermore, the algorithms must be suitable for the real-time implementation. The hardware platform must be able to process huge amount of information in video data stream. From above problem definition follows, that, to design a successful traffic sign recognition system, one must execute all kind of image processing operations to finally detect, classify, or recognize the traffic signs.
Introduction
In the last three decades there was an increase of road traffic, although the number of people killed or seriously injured in road accidents has reduced. This indicates that even if our roads are now more overcrowded than ever before, they are safer due the main advances in vehicle design, such as improved crumple zones and side impact bars. This can also be assigned by passive technology, like seat belts, airbags, and antilock braking systems. According to the department for transport [18] the UK road traffic has increased by 70 percent since 1970 and the number of people killed or seriously injured in road accidents has reduced by 52 percent. We can also see in Figure 1 the same trend of traffic accidents in North South Wales in Australia. The fatality rate per 100000 population has declined dramatically over the last three decades. The most recent fatality rate is approximately the same as in 1908, however there are now approximately 27 times more motor vehicles as in 1908.
Difficulties in detecting and recognizing traffic signs
At first sight the objective of TSDR is well defined and seems to be quite simple. Lets consider a camera that is mounted into a car. This camera captures a stream of images and the system detects and recognizes the traffic signs in the retrieved images. For a graphical view see Figure 3. Unfortunately there are, besides the positive aspects, also some negative aspects.
The positive aspects of TSDR is the uniqueness of the design of traffic signs, colours contrast usually very well against the environment, the signs are strictly positioned relative to the environment and are often set up in a clear sight to the driver.
Previous work
The research of TSDR started in Japan in 1984. Since that time many different techniques have been used, and big improvements have been achieved during the last decade. Besides the commonly used techniques there also exist some uncommon techniques like optical multiple correlation. This technique is presented by, the well know trade-mark, P.S.A. Peugeot Citroen and the University of Cambridge.
One of the most important works in this field is described by Estable et al. [27] and Rehrmann et al. [63] research of Daimler-Benz autonomous vehicle VITA-II. Daimler supports the traffic sign recognition research extensively. Its research group also reported papers concerning colour segmentation, parallel computation, and more. The traffic sign recognition system developed by Daimler is designed to use colour information for the sign detection. The recognition stage is covered by various neural networks or nearest neighbour classifiers [82]. The presence of colour is crucial in this system and is unable to operate with weak or missing colour information. Their biggest advantage is the library of 60000 traffic sign images used for system training and evaluation.
Objectives
The main objective of this paper is the explanation of several techniques, based on computational intelligence, utilized in TSDR systems. Besides that, we also describe the sequence of the executed parts to develop a successful TSDR systems. We can find all different kind of techniques proposed to TSDR, but we emphasize the use of Support Vector Machines (SVM), Neural Networks (NN), and Evolutionary Computing (EC). While the research continued it became clear that the chosen techniques were one of the most widely used in this specific field. Finally, we will give an overview of the researched papers in the field of TSDR.
Artificial Intelligence versus Computational Intelligence?
The title of this paper can be a little bit confusing, because there is no unifying opinion among researchers which specific methods belong to Artificial Intelligence (AI) and to Computational Intelligence (CI). It is also not clear if AI is a part of CI, or the opposite. Or maybe they are not even parts of each other. Subfields of AI are organized around particular problems, applications, and theoretical differences among researchers. Most researchers threat CI as an umbrella under which more and more methods are slowly added. For instance, Engelbrecht [22] used in his books the following five paradigms of CI: NN, EC, swarm intelligence, artificial immune systems, and fuzzy systems. In contrary, a few published books sponsored by the IEEE computational intelligence society tend to see computational intelligence as “a consortium of data-driven methodologies which includes fuzzy logic, artificial neural networks, genetic algorithms, probabilistic belief networks and machine learning” [13]. In general prevails that biological inspiration is a very important factor in CI, but the whole Bayesian foundation of learning, probabilistic and possibilistic reasoning, other alternative methods to handle uncertainty, kernel methods (SVM), information geometry and geometrical learning approaches, search algorithms and many other methods have little to no biological connections. Another problem is where to draw the line; some neural methods are more neural than others.
Traffic sign detection and recognition system
The identification of traffic signs is usually accomplished in two main phases: detection and recognition. In the detection phase we can distinguish the following parts: pre-processing, feature extraction, and segmentation. As we can see a whole chain of image processing steps are required to finally identify the traffic signs. The first step in the detection phase is pre-processing, which may include several operations. These operations corrects an image which is influenced by noise, motion blur, out-of-focus blur, distortion caused by low resolution, etcetera. Secondly, feature images are extracted from the original image. These feature images containing relevant information of the original image, but in a reduced representation. Thereafter, the traffic signs has to be separated from the background. Meaning that regions of constant features and discontinuities must be identified by segmentation . This can be done with simple segmentation techniques and with the more sophisticated segmentation techniques. After the segmentation phase follows another feature extraction part, but this time based on high level image analysis . In the last part of the detection phase are the potential traffic signs detected from the segmented images, by using the extracted features of the previous part. The efficiency and speed of the detection phase are important factors in the whole process, because it reduces the search space and indicates only potential regions. After detection we can further analyze the image with several operations and modify it or extract further necessary information of it. Thereafter, in the recognition phase, the detected traffic signs can be classified into the necessary categories.