On Road Vehicle Detection using Shadows

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1 On Road Vehicle Detection using Shadows Gilad Buchman Grasp Lab, Department of Computer and Information Science School of Engineering University of Pennsylvania, Philadelphia, PA Abstract Autonomous vehicles and Driver Assistant System are required to have situational awareness of their surroundings, especially for the actions and movements of other vehicles on the road. The first step in accomplishing such a system is vehicle detection. In this paper we use shadows as the main cue for vehicle detection. We present a methodical way for finding a vehicle shadow and adding more constraints in order to get a more robust vehicle detection. 1 Introduction The road environment is very dynamic and fast changing. One of the main tasks of any vehicle operator, be it a human driver or an autonomous vehicle, is to track the movements and actions of other vehicles on the road. This task is essential to safe lane following, passing, negotiating an intersection and most important to prevent collisions. Obstacle avoidance can be achieved by a number of different sensor platforms from ladar to radar and vision systems. The dynamic characteristics of obstacles can be detected using a verity of sensors. However, recognizing a vehicle not just as a dynamic obstacles but actually as a vehicle has many advantages. We have knowledge domain about the behavior of vehicles which is very useful e.g. a static vehicle parking by the road side can become dynamic very fast and should be recognized as a vehicle. The main sensor used for object recognition is vision. Vehicle detection using vision based sensors is very challenging due to huge variabilities in in vehicle appearance. Vehicles may vary in shape, size and color. The appearance of a vehicle depends on its pose and lighting condition on the road. Detection of vehicles from a moving vehicle is even harder due to the cluttered environment, the self movement of the vehicle, and the requirement for close to real time processing. The high computational requirements of 1

2 an on board vehicle detection system suggests that searching the whole image to locate a potential vehicle is not an effective way. The majority of methods reported in the literature[5] follow tow basic steps: 1) Hypothesis Generaton (HG) where the locations of possible vehicles in the image are hypothesized and 2)Hypothesis Verification (HV) where tests are performed to verify the presence of vehicles in an image.the objective of the HG step is to quickly find candidate vehicle locations in the image for further exploration. Various approaches have been proposed in the literature [5], mathods based on optical flow[2], IPM [1], color, symmetry, texture, corners and edges [3]. In this paper we use shadows as a visual cue that can finger to a possible location of a vehicle. This cue is augmented with other constraints such as road detection and geometrical constraints on the size of the vehicle in the image to create a robust and efficient system for locating candidate locations in the image where a vehicle can be found. The output of the system will be used by a trained classifier that will verify that the candidate is indeed a vehicle and it can be passed on to the tracking system. 2 Vehicle Detection System The first step of the Vehicle Detection System is to do a quick and dirty segmentation of the image to find regions that are possible locations of vehicles. These locations can then be passed to a trained classifier that can verify if the candidate location is a vehicle indeed. In [4] it was shown that the area underneath a vehicle is distinctly darker than any other areas on an asphalt road. The problem of finding the shadows can be reduced to finding the correct thresholds on the intensity levels. However the intensity of the shadow beneath the vehicle is dependent on the overall illumination, time of day and road color, which suggests that we cannot use a constant threshold. Instead in [6] Tzomakas and Seelen proposed to estimate the high-end of the threshold by analyzing the gray level of the free driving space - the road in front of the vehicle. Specifically, a normal distribution was assumed for the intensity of the free driving space. The mean and variance of the distribution are estimated using MLE. The high threshold of the shadow area is defined as the limit where the distribution of the road gray values decline to zero on the left of the mean, which is approximated by µ 3σ The rest of this paper is organized as follows: In Section 2, we present the vehicle detection system. The results are described in Section 3. In section 4 we summarized the learned lessones and discuss future work. where µ is the mean and σ is the standard deviation. In this paper the free driving space is defined as a window in the bottom and center of the image in the size of 60%X40%. An algorithm that can find the free spac- 2

3 ing space e.g. using IPM as in [1] and assuming a mixture of Gaussian distribution on the road color can give a better performance when dealing with shadowed roads In the next step we cluster together the shaded segments to create the base line of the vehicle candidate. The thresholded image is a binary image where blobs refer to car shadows. The width of the bounding box around each block is the base line of the winodow around the vehicle. The upper part of the vehicle is not detected by the shadow method. Therefore an aspect ratio of one is assumed for every vehicle. Shadows can be found in the full scene and not only under vehicles therefore we need to introduce further constraints to make the Hypothesys Generation process more robust. First, we assume that another process in our vehicle can detect the borders of the road. Based on this knowledge we only look for shadows on the road. Figure 1: 3D Scene Geometry Second, 3D geometry (Fig. 1) is used to calculate constraints on the possible scale of the candidate windows based on the height of its base in the image. The distance z to the vehicle can be calculated based on the height of the vehicle in the image y by the following equation. h z = cos(φ θ) Where h is the height of the vehicle above the road, φ is the camera angle, and θ is the angle between the candidtate window bottom and the center of the image. The perspective equation gives the 2D position for a 3D point. It is derived using the simple geometry of similar triangles as shown in Fig. 2. Figure 2: Perspective Equation Since the ratios of the sides of similar triangles are equal, or Y eyez = y z + eyez Y = y eyez z + eyez This equation is also used to get the object width: 2dW = w eyez z+eyez or w = 2dW s 1 eyez Where s is the scaling factor and eyez z+eyez is the focal length. If the width of the candidate window is smaller then a regular car we dismiss it, if the width is larger then a width of the car, e.g when the road is shadowed by a bridge, we break the window to smaller windows with the default car width of 2m. 3

4 3 Results Figure 3: A scene from the data set The system provides promising results. The algorithm was tested on a urban highway scene. Fig. 3 is a typical image from the data set. The highway has high walls on the side and multiple bridges crossing above the highway. The highway is often shadowed by the overhead bridges, which makes it very difficult for any system that looks for shadows. Despite the hard environment the system identified the correct locations of the vehicles around 80% of the time. It was blinded by the shadows while going under the bridge, where the free driving space is shadowed had similar intensity levels as the vehicles shadows. This problem occurs only for very short periods, 2-3 frames and can be fixed easily by tracking the previous known locations of vehicles. Another problem that was caused by the bridge shadows was while vehicles crossed over shadows, under the bridge the car shadows then coincide with the bridge shadow. Using a mixture of Gaussian model for road color instead estimating the road color from a constant window infront of the vehicle should yield better performance. 4 Summary and Future Work In this paper we presented a system for detecting possible locations of vehicles in an image using shadows. The algorithm is very efficient and performs well even in hard (shadowed) environments. The system is only one step towards a full vehicle detection and tracking system. The full system will include a verifying mechanism for the candidates provided by the system that was described in this paper, and a tracking module to track the vehicles in the image over time. The system can be improved by using known features of car shadows. For example car shadows should be almost horizontal and the edge transfer would be from bright beneath the edge to dark above the edge, using a horizontal edge detector should improve the results. Currently the system breaks large shadows to small candidate windows, a better approach could be to locate regions in the shadows which are even darker, since the car shadow should be darker than its surrounding. These improvements and the building of a complete system is left for future work. References [1] Bertozzi M. and Broggi A. Gold: A Parallel Real-Time stereo Vision System for Generic Obstacle and Lane Detection, In IEEE Trans. Image Processing, Vol. 7, pp , [2] Giachetti A., Campani M., and Torre V. The Use of Optical Flow for Road Navi- 4

5 gation, In IEEE Trans. Robotics and Automation, Vol. 14, no. 1, pp 34-48, [3] Matthews N., An p., Charnley D., and Harris C. Vehicle Detection and Recognition in Grayscale Imagery, In Control Eng. Practice, Vol. 4, pp/ , [4] Mori H., and Charkai N. Shadow and Rhythm as Sign Patterns of Obstacle Detection, In International Symposium on Industrial Electronics, pp , [5] Sun Z., Bebis G., and Miller R. On Road Vehicle Detection: A Review, In IEEE Transactions on Pattern Analsys and Machine Intelligence, Vol. 28 No.5 May [6] Tzomakas C., and Seelen W. Vehicle Detection in Traffic Scenes Using Shadows, Technical Report 98-06, Institut fur Neuroninformatic, Ruht-Universitat, Bouchum, Germany,

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