Analyzing Traffic Density in Images with Low Temporal and Spatial Resolution

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1 Analyzing Traffic Density in Images with Low Temporal and Spatial Resolution Michael Claveria Dept. of Information and Computer Sciences University of Hawai`I at Mānoa 1680 East-West Road, Honolulu, HI 96822, USA Kyungim Baek* Dept. of Information and Computer Sciences University of Hawai`I at Mānoa 1680 East-West Road, Honolulu, HI 96822, USA ABSTRACT The increasing proliferation of traffic monitoring technology has brought about sophisticated techniques for traffic monitoring such as motion tracking using active or optical sensors. Image processing techniques to identify vehicles and track velocity are possible using real time video feedback from traffic cameras along major roads and highways. However, many cities have limitations on camera and equipment quality which obstruct traffic monitoring processes. In Honolulu, the traffic images posted on the traffic monitoring website have a 3 minutes delay between frames. This makes it impossible to perform vehicle tracking based on those images. Variations in camera angles and low spatial resolution also make the task of monitoring traffic more difficult. In this paper two simple traffic density estimators with two different background models are implemented and compared to each other. The estimator first separates traffic foreground from road background using moving average or codebook methods. A modified Hough transformation identifies potential road area and then the traffic density is quantified as percentage of traffic contained within the road area of an image. These techniques deal with the limitations of traffic images with low spatial resolution and low frame rate. Categories and Subject Descriptors I.4.8 [Image Processing and Computer Vision]: Scene Analysis General Terms Algorithms, Performance, Experimentation Keywords Traffic density analysis, Background, Foreground, Moving average, Codebook, Classification 1. INTRODUCTION Traffic monitoring devices have become more commonplace in the lives of everyday motorists as techniques for detecting traffic have become more sophisticated over time. According to a recent urban mobility report, traffic congestion has been increasing over the years and costs the nation $121 billion a year in wasted travel time and fuel [1]. A real time traffic analysis can provide users with information for real time travel options to avoid congestions based on current road transit and parking conditions. It can also * Author to whom correspondence should be addressed. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. IVCNZ 14, November , Hamilton, New Zealand Copyright 2014 ACM /14/11 $ aid in the future development of transportation systems by providing data on traffic patterns. One common approach to traffic monitoring includes using active sensors which are typically radar, laser, or acoustic based (e.g. [2, 3, 4]). These sensors are considered active because they detect the objects by measuring the travel time of a signal emitted by the sensors and reflected by the objects [5]. The main advantage of active sensors is their ability to measuring quantities like distance without requiring powerful computing resources and sophisticated processes. Optical sensors (cameras), on the other hand, are referred to as passive sensors because they obtain data in a nonintrusive way. The advantage of passive sensors is the lower cost for implementation and maintenance. Passive sensors can also provide visual information that, when processed, can be used for tasks such as object identification (pedestrians and other objects) that active sensors cannot. They can adjust the angle of viewing much easier than radar sensors can adjust to area of effect. In the past 30 years there have been tremendous strides in using optical sensors to quantify traffic patterns on major roads and highways. The vision based traffic monitoring process has improved with the growth of the computer vision field, the proliferation of feasible technology, and the exponential increase in processor speeds. In one of the classic papers classifying traffic using a computer vision technique, Riddler et al. [6] modeled each pixel in a frame using a Kalman Filter to predict the traffic density. Koller et al. [7] then used this model to create an automatic traffic monitoring application. The model was robust to lighting changes in the scene, however, it recovered slowly and did not handle bimodal backgrounds well. Wren et al. [8] used a multi-class statistical model, Pfinder, for tracking objects and a single Gaussian model for each pixel in the background. This produced good results, however, its application was limited to indoor scenes and not tested on outdoor scenes. Friedman and Russell [9] used a pixelwise EM framework for detection of vehicles. They classified pixel values into three separate distributions corresponding to road color, shadow color and vehicle color. For this study it is not clear how this works for pixels that present multiple background colors such as those resulting from repetitive motions or reflectance. Stauffer and Grimson [10] used a mixture of Gaussians for each pixel to deal with lighting changes and repetitive motions of objects. It successfully tracked people and cars in an outdoor environment and adjusted to changes in the background over time, but worked with video processing with high temporal resolution. Li et al. [11] proposed a real-time virtual loop detector (mimicking the idea of a physical loop detector) by using a boosted support vector machine classifier to probabilistically determine the traffic density state. They achieved an average accuracy at around 95% under different daytime illumination and weather conditions. However, they also have a high enough frame rate to perform tracking sequences in their project.

2 Official Hawaii state traffic cameras located on over 90 intersections in the island of Oahu report images of the traffic scene to a Honolulu Country traffic website every 3 minutes [12]. Such extremely low refresh rates provide traffic snapshots of individual locations, but make it difficult to track traffic over time. It is also difficult for an individual to process multiple traffic images around the entire state all at once. A solution is to automate the process to let a computer analyze traffic images and report the traffic density conditions. Two popular methods of measuring traffic density include counting vehicles by means of motion sensor devices along the road and using GPS systems to track cell phone location of a motorist on the road. Automated computer vision processes, especially on the island of Oahu, provide another such opportunity for vehicle analysis due to the proliferation of cameras along the non-freeway roads of the island. Image processing is also much more cost effective over sensors and motion detectors since it does not require additional hardware setup and maintenance beyond the original cameras. While many of the previously proposed vision based traffic analysis methods were successful in tracking and identifying objects within images, these methods are not fully applicable to analyzing traffic density in images taken from traffic cameras on Oahu. The publically available traffic images from the Honolulu traffic cameras have a low spatial resolution and large interval times between successive images from one location. With a 3 minutes interval, there is little chance of a car in one images being present in a future image thus methods that rely on motion analysis across images are not applicable. Some methods that were designed to work indoors do not capture the changes in lighting and shadows that occur with outdoor traffic conditions. Others that used algorithms to identified individual objects could only work for images with high enough spatial resolution. Low spatial resolution makes identifying individual cars difficult since a group of cars looks like one large clump in a highly pixilated image. Finally, a few methods use complex algorithms that might identify traffic down to the single pixel, but otherwise could not be used practically due to long processing times. Since traffic is classified as qualitatively as heavy, light, etc. a reasonable traffic estimation is sufficient for classification. The model presented in this paper is part of a process to create and maintain a traffic monitoring system that accurately and efficiently displays qualitative traffic density conditions in real time for locations with an installed traffic camera. An application for this work is to extend automated traffic monitoring coverage to the arterial roads under the surveillance of traffic cameras. When used together with other popular applications that already measure traffic congestion along freeways and major roads, it would help to complete traffic monitoring around the entire island. This information could be used in several applications in the future such as developing a real-time automated best route finder or monitoring traffic conditions over a long period of time. Due to real time gathering process of the images, this preliminary model is a streamlined approach with computing times less than a second. For simplicity, this work focused on images from four cameras placed on the intersections of: University Ave. and Dole St., King St. and Bishop St., King St. and Punchbowl St., and Beretania St. and Punchbowl St. (Figure 1). A sequence of 30 training images from each site are gathered and used to build background models that are subsequently used to estimate the traffic density of a set of 20 test images from those sites. Figure 1. Sample traffic images of the four different intersections (University avenue and Dole street, King and Bishop streets, King and Punchbowl streets, and Beretania and Punchbowl streets). 2. TRAFFIC DENSITY ESTIMATION 2.1 Background Modeling Detection of foreground traffic requires creating a background model using training images. The two different approaches used to model the background are Moving Average and Codebook methods. Moving Average Moving average models the background by creating an ideal no traffic image to use as a base image for traffic comparisons. Images are averaged over time with higher weight given to more recent images. With newest data point of weight n, and each successive data point decreasing in weight by 1, we calculate the weighted moving average for n considered points by npt ( n 1) pt 1 2pt ( n 2) pt ( n 1) MAt n ( n 1) 2 1 where weight n is given to the most recent image p t. When incorporating a new image after the n images, replace the oldest image following the equations: Total Numerator MA t 1 Total p p Numerator np t ( n 1) Numerator n ( n 1) 2 1 where Total t is p t + + p t (n 1). t t Total Due to the frequency of cars passing, it is difficult to find an image without vehicles in it that could serve as an ideal background image. One means of getting around this is by constructing an ideal background image by averaging out vehicles over time. Calculating a weighted moving average of a set of images effectively creates a no traffic image (Figure 2). This method finds the mean pixel intensity over a series of training images. The idea is that most of the images will have the ideal background pixel while a few others will have a car in the background for a given pixel. Creating the average image will theoretically resemble the background more than the vehicles. This method gives weighted priority to more recent images to better reflect current conditions. After each image is evaluated the image is added to the vector of test images to enhance the current sample of images. Thus the system will learn and theoretically be more accurate as it gathers more images over time. It can also adapt to changes in the background over time. Figure 2. The moving average images for each of the four intersections generated from 30 training images from each site shown in Figure 1. t

3 Codebook A second method of constructing a background model uses a structure called codebook to differentiate between foreground and background [13, 14]. Codebook looks at frequencies of each pixel value over a sequence of images. If there are enough values that extend beyond a single range of values, codebook accepts discrete ranges of values (Figure 3). Figure 3. A simple illustration of codebook formation reproduced based on the figure in [13]. The original pixel values grow the box bounds over time and larger values that occur later in the graph create two discrete box ranges. If this is the finalized codebook background model, any values that fit into the range of those two boxes at the end will be considered part of the background. In this work, we model codebook using simple boxes that cover common values as seen over time. Each box is defined by min and max thresholds for each of the three color axes. The box grows (i.e. the bounding thresholds expand) if the newer background samples fall within a learning threshold above the max or below the min threshold. Any background samples that fall out of the box will start a new box. Codebook method can handle pixels whose values might change dramatically but still keep to a discrete range. It is more robust in handling changes in the background than moving average and can adapt to shifting shadows and lighting with enough training images, but it is more computationally intensive than the moving average method. 2.2 Foreground Extraction Process Once the background model is constructed from the training images by the moving average or the codebook method, it is converted to grayscale background image and the foreground traffic in a test image is found by subtracting the background image from a grayscale version of the test image. Then, dilation and erosion operations are applied to the resulting image using a 3 3 block structuring element to remove noise and to fill the holes of larger regions [15]. Thresholding is applied to the image after the morphological operators to produce a black and white image. The white pixels in the binary image should represent the area where vehicles occupy the scene. Finally, a connected component analysis is performed to remove small regions and to fill in gaps between regions that should be connected [15]. (a) (b) (c) (d) Figure 4. (a) Original image. (b) Moving average background. (c) Result of subtracting background (b) from (a) and applying threshold. (d) Result of the connected components performed on (c) and superimposed over the image (a). Figure 4 shows the results of the foreground extraction process. Image (d) shows that the morphological operators followed by connected component analysis cleans the foreground image (c) formed by background subtraction. Note how some of the extraneous features, such as trees, as well as some smaller cars in the background are removed in image (d). By adjusting parameters one can vary the regions identified as foreground. 2.3 Extracting Region of Interest (Road Area) The traffic images have background area that cannot contain traffic but can nevertheless throw off results due to excess noise. Such areas include the sky, buildings in the background, trees and sidewalk area. For more accurate results it is important to identify the region of potential traffic, i.e. the road area. Road area can be manually found by cropping out buildings, sidewalk and non-essential foreground areas, but can be troublesome especially when having to manually crop each image. One possible approach to this problem is to use Canny edge detection [16] to calculate the outlines of objects within the background image and then use a modified Hough transformation [17] to detect the extended road lines. In each of the averaged images, the longest lines in the image are likely to correspond to outlines of the main road extending through the image. One can then calculate the area within the longest lines to estimate the road region. Classification of traffic density is done using the found foreground traffic area together with the calculated region of interest road area. (a) (b) (c) (d) Figure 5. (a) Background image. (b) Results of the Canny edge detection done on the image (a). (c) The lines calculated from the thresholded Hough transformation. (d) The road area in red calculated by extending the lines in (c) and filling in the area within those boundary lines. Figure 6. Calculated road areas for the other three intersections. Although the areas do not capture the perfect road area, they offer a reasonably good approximation of region of interest which can be used to filter out noise in nonroad portions of the images. Using Canny edge detection on the background image (Figure 5(a)), we get the outlines of objects in the image (Figure 5(b)). After finding the edges in an image, applying a thresholded Hough transformation captures the long lines in the image which signify the road lines (Figure 5(c)). The Hough transform keeps track of any line with the equation y = mx + b for slope m and y- intercept b. Any line can be uniquely identified by the pair of

4 slope and y intercept (m, b). In the implementation, the polar coordinate representation of a line is used to construct the parameter space due to the problems with (m, b) representation unbounded parameter domain and infinite m for vertical lines. At each pixel the algorithm determines if there is enough evidence of an edge or a line segment that connects through that pixel. Each unique line contains a particular evaluation value in the algorithm and more evidence of specific line parameters will increase the evaluation value of that line. The lines with the largest value are then chosen as the most visible lines in the image. Since road lengths span a large portion of the image, a high enough threshold will isolate those large lines. If we extend the lines and fill in the area, we find the road region in the image (Figure 5(d)). Figure 6 shows the extracted road areas for the other three locations. 2.4 Classification The easiest way to classify the traffic density would be to count the number of foreground pixels located in the road area. Foreground traffic would only be counted if it is within a buffer region of the calculated potential road area. Since different traffic camera views have different sized road areas, traffic data can be extracted based on the ratio of the calculated foreground area to the potential road area of that camera s image. This ratio can be broken down into qualitative classifications of traffic. For example, a ratio for a certain intersection might mean light traffic while 0.5 might indicate heavy traffic. Since the classification of images between light, medium, heavy, etc. remains arbitrary, specifying exact cut off values for a classification would best be done after monitoring a system over time. This way one could make better judgments as to the accuracy of such measurements as judged by comparison to human interpretations of the images. Since assignment of the classification is a judgment call and may differ based on the intersection and the camera, no defined classification has been reached in this work at the moment. Such a classification can be done through experimental analysis and can be compared to human interpretation for evaluative purposes. 3. RESULTS The estimation system was implemented in C++ with OpenCV library. 200 images were gathered for the classification process with 50 images from each of the four different camera locations in the island of Oahu. 30 of those 50 images were chosen at random and used to train the codebook and moving average models. The other 20 images from each site were evaluated for traffic content. The output for each tested image is a number corresponding to calculated traffic ratio in that image. Due to the limited space, results for four test images for each site are shown in Figure 7. The numbers below each image indicate the traffic density percentage calculated using moving average and codebook methods. MA represents the traffic density calculated from the moving average method of identifying foreground traffic and CB represents the codebook methods of identifying foreground traffic. The four test sites show significantly different results in terms of how they were classified using the two different methods. 3.1 University Ave. and Dole St. Codebook and moving average had less variation between them at University and Dole relative to the variation between the two methods at the other three sites (first column in Figure 7). They both performed similarly for this particular intersection, with the greatest issue being the perspective of the camera. Both methods appeared to underestimate the traffic in images with many small cars in the distance and overestimate the traffic in images with vehicles closer to the camera. The two way traffic presented another challenge as traffic in the outgoing lane (with vehicles oriented toward the upper right of the image) would occupy greater area than the incoming lane (vehicles oriented toward the lower left of the image) due to the position of the camera. This particular intersection had limited issues in lighting which would prove to be a significant issue in the analysis of the other intersections. Overall, this intersection is easy/medium to classify relative to the other intersections. MA: CB: MA: CB: MA: CB: MA: CB: Figure 7. Traffic density quantified as percentage of traffic within the extracted road area for the four intersections. (From left column to the right column: University avenue and Dole street, King and Punchbowl streets, King and Bishop streets, Beretania and Punchbowl streets.) 3.2 King St. and Punchbowl St. For the set of images used in this work, King and Punchbowl appeared to be a busier intersection than the other three (second column in Figure 7). Codebook did a much better job of correctly identifying low traffic values for the images with few vehicles in them. The problem with moving average came from the differences in lighting between the images. Although shadows were minimal, the effect of changes in lighting over time meant that the road appeared shiny and light in some images while darker in others. The estimation process using moving average was picking up significant portions of the road as foreground since it is different in color from the background image. Codebook, on the other hand, could adapt to the lighting changes. While the camera on King and Punchbowl had a similar perspective view to the one on University and Dole, the one way traffic made computing the density easier. Traffic for this intersection is easy to classify because of its closer proximity to the road, minimal shadow effects, and one way traffic flow.

5 3.3 King St. and Bishop St. King and Bishop had significant lighting changes within the span of the twenty test images which threw off some results for the two methods (third column in Figure 7). Moving average based estimation had high calculated traffic much like that of King and Punchbowl due to the variability in lighting and the dramatic shadow effects in the downtown environment. Codebook did a decent job adapting to the lighting issues except for three images in the test set with values around 44, 65, and 57. (Only one of the three images is shown in Figure 7.) These three images had very little traffic in them and the estimation problem most likely stemmed from the unique shadow conditions in those three images. Unfortunately, the training images did not have that particular shadow pattern as the large shadow appeared to have entered over the course of a few minutes. One of the problems with the low temporal resolution is that dramatic changes in shadows can occur over the course of a few frames. Other than those three images, codebook did a good job of detecting limited traffic and correctly identifying higher levels of traffic. This particular intersection is difficult to classify because of the dramatic shifts in lighting and shadows most likely due to the surrounding buildings. 3.4 Beretania St. and Punchbowl St. Beretania and Punchbowl had smaller traffic density calculations and overall it appeared to have much less traffic in the images (fourth column in Figure 7). Both moving average and codebook methods seemed to have trouble picking up the small cars in the background. The intersection is at a four way stop and the camera perspective is pulled back from road more than the other intersections. Moving average based estimation had consistently low values except for some where the shadow of a tree took up a large section of the road area. It did not do a good job estimating traffic for this intersection. Codebook based estimation appeared to identify the few images with heavier traffic. The vast majority of vehicles in the image appeared clumped together in the background, most likely due to the traffic light causing them to stop. Overall this intersection and intersections of this type are probably the most difficult to classify. The four way intersection requires a camera to pull back in order to capture traffic coming from all four directions. This makes the resolution even worse. Since traffic was mostly in the background, any traffic moving from right to left or left to right at that intersection would appear more prominently. This was the case in a few test images. Shadow effects are also a concern as a large tree shadow progressively emerged onto the road area through successive images. 4. DISCUSSION 4.1 Problems with Moving Average The first classification attempt using moving average as a background model performed poorly for images with high levels of contrast from shadows and lighting. Moving average resulted in a neutral background image so shadow or light details in an original image are identified as foreground traffic after background subtraction. Certain intersections with high levels of trees and buildings in the surrounding area, such as the one in King and Bishop streets, give off patterned light areas at certain times of day (Figure 8). Implementation of codebook helped to correct for lighting changes since the training images captured over the course of the day had enough shadow and lighting effects for codebook to adapt to the different background conditions (Figure 9). There is a large difference between codebook and moving average for several of the test images from King and Bishop streets, and King and Punchbowl streets. Moving average tends to overestimate foreground traffic by counting different lighting patterns as traffic. (a) (b) (c) (d) Figure 8. (a) This traffic image from King and Bishop streets shows interesting shadow and lighting effects. (b) The moving average background image taken over the course of one day creates a neutral image. (c) Background subtraction and thresholded image reveals several lighted areas are perceived as foreground. (d) The calculated foreground image is superimposed over original image (a). In general foreground extraction with codebook resulted in respectable outcome for relative traffic. The process of evaluating methods is somewhat imperfect since there is no best measure for a qualitative process of evaluating traffic density. However, codebook has discernable problems identifying background on a larger level although some pixels around the larger areas were added. These were not significant enough to warrant concern since traffic density analysis does not require such precision. Figure 9. The codebook result of the same street image as in Figure 8(a) is shown in the middle. It has a faded identification of lighting which, after thresholding and postprocessing, results in the image on the right. Such a classification would be successful since there are no cars in the image and there should not be any white foreground pixels. 4.2 Perspective View of Camera The camera position differs for different intersections and rarely is head on with the traffic. This results in the skewing of perspective angles. For example, the University and Dole traffic camera perspective makes cars in the lower right-hand area of the image larger while causing cars in the incoming lane to appear smaller (see the first image in Figure 7). As with most images, the vehicles further away from the camera take up less space than those closer to the camera which can skew results for images with many cars in the distance or a few large cars up close. Some cars in the distant background are treated as noise if their size is not large enough to register for the connected component threshold. 4.3 Different Sites The different intersections provided different results for the different models. On the University Ave. and Dole St. intersection the codebook and moving average performed similarly on the analysis with the weakness that both tend to disregard cars in the distant background. The images from the intersections of University Ave. and Dole St., and Beretania and Punchbowl streets tend to have a smaller proportion of traffic relative to those of the other two intersections. Due to the camera angle, individual cars tend to be smaller in those intersections as opposed to King and Bishop streets, and King and Punchbowl streets.

6 From the test images, King and Punchbowl streets, and King and Bishop streets have the most variance in lighting and shadows between images. This caused the largest difference between moving average and codebook calculations within the same image. King and Punchbowl streets appear to have more traffic in the test images as compared to the other intersections. University Ave. and Dole St. provide the additional challenge of two way traffic, while Beretania and Punchbowl streets have even more complications as a four way stop. There is no way for the current model to take into account heavy traffic in one direction but not the other since it only measures total traffic throughout the entire image. One possibility is to have multiple traffic models, one for each lane and direction. However, that runs into the additional challenge of identifying the car direction which is difficult for a single image or a sequence of images with a large between-image interval. 5. CONCLUSIONS AND FUTURE WORK The method studied in this paper provides a simple and efficient approach to traffic density estimation when faced with the limitations of low spatial resolution and large intervals between successive images. The 3 minute interval rate between images makes tracking vehicles virtually impossible and negates the usage of sophisticated tracking algorithms. Low spatial resolution makes it difficult to identify or find the exact outline of a vehicle since the image is highly pixilated. Moving average as a background model captured traffic well under ideal lighting conditions but codebook worked better for images with heavy shadow and lighting issues. A modified Hough transformation did a decent job of identifying the road lines in an image which were used to identify the road area. The simple traffic density estimation method had trouble dealing with camera perspective and had differing levels of accuracy for each of the four tested traffic sites due to unique challenges of each intersection. This work suggests an initial procedure for classification of traffic density in real time which can be built upon by implementing more advanced techniques in the future. Even though it is simplified to deal with technological constraints, this traffic density estimation system does a sufficient job of analyzing traffic patterns around the island of Oahu. There are a number of ways to improve current work so that it can deal with some of the difficulties previously discussed and more complicated situations. For example, a transformation of the street image changing the viewpoint to a top down view could help with the perspective issue. This makes vehicles in the distance take up roughly equal area compared to those similar sized vehicles closer to the camera. Another problem to consider is that cars of similar color to the road are sometimes removed from the image. Future implementations may consider a separate color component, such as hue, to avoid the problem. Better filtering methods could also be used such that the threshold can be altered to recognize more subtle differences between vehicle features and the roads. One more future goal could be to implement a capability of recommending a best path between locations that avoids heavy traffic as detected by the cameras. One can expand on the results of four different locations to incorporate all 90+ traffic locations where the Honolulu Traffic Cameras are installed. Individual traffic images from a location do not provide much information, but as a network they can work together to recommend a path of least traffic congestion from a starting point to a destination. 6. REFERENCES [1] Schrank, D., Eisele, B., and Lomax, T TTI s 2012 Urban Mobility Report. 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