Effectiveness of Machine Vision Techniques in Traffic Monitoring and Dimension Metrology

Various techniques used for road-traffic monitoring rely on sensors which have limited capabilities, are inexible and often, both costly and disruptive to install. The use of video cameras (many of which are already installed to survey road networks), coupled with computer vision techniques offers an attractive alternative to current sensors. Vision based sensors have the potential to measure a far greater variety of traffic parameters compared to conventional sensors. There are two vision based traffic-monitoring systems. The first is a number-plate recognition system. This is capable of monitoring the output from a video camera and detecting when a vehicle passes by. At this moment an image is captured and the vehicle's number-plate is located and deciphered. The second system is a generic roadtraffic monitoring sensor which utilises model based techniques to track vehicles as they maneuver through complex road scenes. The position of the vehicle in the image is transformed to the vehicle's position in the real world enabling, among other things, vehicle speed and path to be easily measured. The development of each system is described in detail and results from testing the systems on images from real traffic scenes are presented. http://www.ijecbs.com Vol. 2 Issue 2 July 2012 Introduction: 1.1 Road-traffic Monitoring: Road-traffic monitoring involves the collection of data describing the characteristics of vehicles and their movement through road networks. Vehicle counts, vehicle speed, vehicle path, rates, vehicle density, vehicle length, weight, class (car, van, bus) and vehicle identity via the number plate are all examples of useful data. Such data may be used for one of four purposes: Law enforcement: Speeding vehicles, dangerous driving, illegal use of bus lanes, detection of stolen or wanted vehicles. Automatic toll gates: Manual toll gates require the vehicle to stop and the driver to pay an appropriate traffic. In an automatic system the vehicle would no longer need to stop. As it passes the toll gate it would be automatically classified in order to calculate the correct traffic. The vehicle's number-plate would be automatically deciphered and the owner sent a monthly bill. Congestion & Incident detection: Traffic queues, accidents and slow vehicles are potentially hazardous to approaching vehicles. If such incidents can be detected then variable message signs and speed limits can be set up-stream in order to warn approaching drivers. Increasing road capacity: Increasing the capacity of existing roads is an attractive alternative to building new roads. Given sufficient information about the status of a road network it is possible to automatically route traffic along the least congested roads at a controlled speed in order to optimise the overall capacity of the network. Currently, road-traffic monitoring relies on the technology of sensors based on radar, microwaves, tubes or loop detectors (Figure 1.1): Radar: For accurately measuring vehicle speed. Microwave detectors: These are usually mounted on a bridge or gantry such that they point vertically down over a lane of traffic. The device emits microwaves which are reacted on the road surface and bounced back towards the sensor. A vehicle passing under the sensor will cause interference to the reacted microwaves which enables the vehicle to be detected. http://www.ijecbs.com Vol. 2 Issue 2 July 2012 Tubes: A rubber tube fixed to the road surface across the width of a lane of traffic forms the basis of this sensor. One end of the tube is closed and the other is connected to a pressure sensor. As each wheel of a vehicle runs over the tube it causes a pressure fluctuation inside the tube which is detected by the pressure sensor. Each pressure fluctuation represents one axle of a vehicle passing over the sensor. Tubes count the number of vehicle axles which pass a particular point on the road allowing vehicle count, vehicle length and class to be deduced. Loop Detector Microwave Sensor Radar Based Speed Sensor Tube Sensor Figure 1.1: Sensors currently in use for road-traffic monitoring Loop detectors: These consist of a large coil of wire buried just below the road surface. As vehicles pass over the coil, the inductance of the coil changes and the vehicle can be detected. From this range of sensors, loop detectors are the most prominent and are used almost universally in traffic light systems. Although an individual detector merely signals the presence or absence of a vehicle, the outputs of several detectors may be collate to deduce http://www.ijecbs.com Vol. 2 Issue 2 July 2012 information such as vehicle speed, length, low rates and density. There are several disadvantages in using such sensors. As they are only capable of detecting vehicles directly overhead, a typical road junction requires the installation of many sensors in order to cover all entry/exit points. They are highly inexible, once installed they may not be moved. Installation is costly and disruptive. Loop detectors are vulnerable to resurfacing or road works, in the USA 30% are out of operation at any one time.Computer vision based monitoring systems will overcome many of these disadvantages. 1.2 Road-traffic Monitoring and Computer Vision: Computer vision is the process of using a computer to extract high level information from a digital image. A typical vision system for road traffic monitoring might appear as in Figure 1.2. The CCD camera provides live video which is digitised and fed into the computer which may well contain some special purpose hardware to cope with the extremely high data rates (10 MBytes/s). Computer vision algorithms then perform vehicle detection,tracking, classiffication or identification via number-plate recognition. Vision is potentially more powerful than any other sensor currently available. The installation of video cameras to monitor road networks is cheaper and less disruptive than installing other sensors. In fact, large numbers of cameras are already installed on road networks for surveillance purposes. A single camera is able to monitor more than one lane of trafic along several hundred metres of road. Vision based systems have the potential to extract a much richer variety of information such as precise vehicle path, vehicle shape, dimensions and colour. With suitable positioning of the camera, a vision system is capable of tracking vehicles as they manoeuvre through complex road junctions Traffic Information Algorithms Computer Vision Digitiser http://www.ijecbs.com Vol. 2 Issue 2 July 2012 Figure 1.2: A typical vision system for road-traffic monitoring or along relatively long stretches of road. A vision system could theoretically have the same powers of observation asa human observer but without the detrimental effects of tiredness and boredom. A fundamental requirement for the success of a vision based traffic monitoring system is that it operates in real time. If each image is 720 by 512 pixels and the camera is producing 25 frames per second then the data rate is in the order of 10 MBytes/s. This may be coped with by the use of special purpose, possibly parallel, hardware. Such hardware tends to implement low level functions such as filtering (convolution) or pixelwise operators which involve very simple operations that must be repeated many times per image. The alternative way of coping with the high data rates is by data reduction, spatially or temporally. Spatial data reduction involves processing only small portions of each image known as regions of Interest. In a typical traffic scene, much of the image is of little interest as it contains buildings, vegetation or pavement. These areas are never likely to contain a vehicle and so it is ludicrous to waste processor time on them. Temporal data reduction is achieved by only processing every nth frame. The amount of temporal data reduction that may be applied is dependent on the particular application. A system for measuring queue length at a set of traffic lights might only need to operate at one frame every few seconds whereas a system for tracking vehicles through junctions must process at least several frames per second. A Number-Plate Recognition System A Generic Road-Traffic Monitoring Sensor http://www.ijecbs.com Vol. 2 Issue 2 July 2012 The first gives an introduction to the principal techniques of number-plate recognition, namely optical character recognition. Several number-plate recognition systems which have been developed around the world are then reviewed. The second section deals with roadtraffic monitoring systems which are non-model based. Non-model based systems have no idea of what a vehicle looks like and are therefore unable to achieve any image understanding. They are able to detect and track objects in the scene but are unable to recognise them. The consequence of this is that one of these systems would respond to an elephant walking down the road as if it were a car. These systems merely detect and track groups of image pixels without understanding what the pixels represent in the real world. Again, relevant techniques are introduced, in this case, motion detection review is then given of several non-model based road-traffic monitoring systems which have been developed. The third section is concerned with model-based road-traffic monitoring systems. The systems described in this section are different because they actually begin to gain an understanding of what is happening in the scene. Using information about the position of the camera relative to the road and knowledge of what vehicles look like, the image is transformed into a full 3D description of the scene. Not only are these systems able to locate objects in 3D real world coordinates but they are also able to recognise vehicles. These systems would not be fooled by an elephant walking down the road. The location and recognition processes are able to extract far more information than the non-model based systems, i.e. vehicle dimensions and shape, direction and precise path. Vehicle dimensions and shape can be used to classify vehicles as car, van, bus, etc. Knowledge about the vehicle shape and scene geometry is represented in models and the techniques of modelbased object recognition are used to locate and track vehicles through sequences of images. This section therefore contains an introduction to the methods of model-based object recognition followed by a review of research into model-based traffic monitoring systems. Number-plate Recognition: In order to achieve number-plate recognition, two processes must be performed. The first is to locate the number-plate and its constituent characters in the image. There are no established methods for doing this and developers are reluctant to publish details of their systems due to the commercial nature of the problem. It can be http://www.ijecbs.com Vol. 2 Issue 2 July 2012 regarded as the most challenging aspect of number-plate recognition. The few systems described in the literature seem to adopt one of two approaches. The first is based on thresholding the image such that number-plate characters are black and the background white. The image is then searched for regions containing several adjacent black blobs which all have similar dimensions to the expected number-plate characters. The second approach is to utilise neural networks although the details of exactly how do not seem to have been published.The second process is character recognition. This is a fairly well developed field in computer vision and several techniques are available. The review of Govindan and Shivaprasad [1] forms the basis of the following discussion which describes the techniques of Template matching, Feature based character recognition and Neural Networks for character recognition. Template matching: This involves the use of a database of characters or templates.There is a separate template for each possible input character. Recognition is achieved by comparing the current input character to each template in order to find the one which matches the best. If I(x; y) is the input character, Tn(x; y) is template n, then the matching function s(I; Tn) will return a value indicating how well template n matches the input character (Figure 2.1). Several common matching functions are: Cityblock Character recognition is achieved by identifying which Tn gives the best value of matching function, s(I; Tn). The method can only be successful if the input character and the stored templates are of the same (or at least very similar) font. Template matching can be http://www.ijecbs.com Vol. 2 Issue 2 July 2012 performed on binary, thresholded characters or on grey-level characters. In the latter case, comparison functions such as Normalised Correlation are usually used as they provide improved immunity to variations in brightness and contrast between the input character and the stored template. Feature based character recognition: This is performed by first extracting significant features from the input character. These features are then compared to a database of feature descriptors for all of the possible input characters. The best matching descriptor provides recognition. The type of feature extracted may be classed as one of the following: Figure 2.1: Template matching _ Features produced by global transformations and series expansions. _ Features derived from the statistical distribution of points. _ Geometrical and topological features. Global transformations and series expansions reduce the dimensionality of the feature vector and can provide some invariance to translation, scale and rotation. Examples include Fourier, Walsh, Haar, Hadamard series expansions and Hough transform, chain-code transform and principal axis transform. Features derived from the statistical distribution of points include Zoning, Moments, n-tuples, Characteristic Loci and Crossing and Distances. These features provide some immunity to small translation and rotation distortions as well as variations in font.Geometrical and topological features provide high immunity to changes in font and are insensitive to small http://www.ijecbs.com Vol. 2 Issue 2 July 2012 amounts of translation and rotation. Typical features might include strokes and bays in various directions (Figure 2.2), end points, intersections of lines, loops and angular relations between lines.Neural networks: These can be used electively for classification [2, 3, 4] and are therefore