1. 1 REAL-TIME IMAGE PROCESSING APPROACH TO MEASURE TRAFFIC QUEUE PARAMETERS. M. Fathy and M.Y. Siyal Conference 1995: Image Processing And Its Applications

2. 2 OUTLINE  Introduction  The queue detection algorithm Motion detection algorithm Vehicle detection algorithm  Results and discussion  Conclusion  Bibliography

3. 3 INTRODUCTION  Measure of traffic queue is required in many situations - Traffic jam - Traffic accidents - Adjusting time in traffic lights  Problems to measure the traffic in real-time - Variations of light conditions - Different shape or size of Vehicles - Geometry of the scene  Objectives of the paper - Measure in real time accurately queue parameters like length or period of occurrence

4. 4 INTRODUCTION  Previous works - Rourke and Bell (1991): Method based in Fast Fournier Transformation (FFT). This method do not measure the length. Very time-consuming. - Hoose (1991): Do not measure length.  Introduction to the algorithm Motion detection Vehicle detection Yes No This approach reduces the computational time

5. 5 MOTION DETECTION ALGORITHM  The image is divided in sub-profiles.  Sub-profiles with different size to compensate: − Effect of the transfer of the three-dimensional view of the camera to a two-dimensional image. − Parameters to the camera like height of the camera, field of view and angle of the optical axes  By knowing the coordinates of 6 reference points of the real-world and the coordinates of their corresponding images to make a geometric correction and measure length.  The size of the sub-profile depends on the resolution and the accuracy required, but the size should be about the length of the vehicle.  A median filter is applied to the sub-profiles to remove the noise. 4 th ave. New york (4-2-06)

6. 6 MOTION DETECTION ALGORITHM  For each sub-profile are calculated the histogram for two consecutives frames First frameSecond frameDifference Motion detected Difference histogram with high values

7. 7 MOTION DETECTION ALGORITHM First frameSecond frame Difference No Motion detected Difference histogram with Low values

8. 8 VEHICLE DETECTION ALGORITHM  Most of the vehicle detection algorithms developed so far are based on a background differencing technique. However, this method is sensitive to the variations of ambient lighting and it is not suitable for real world applications.  The method used here is based on applying edge detector operators because edges are less sensitive to light variations  The edge detector, consisting of separable median filtering and morphological operators, SMED (separable morphological edge detector).  The Edge detector is applied to each sub-profile Motion detection Vehicle detection Yes No

9. 9 VEHICLE DETECTION ALGORITHM  The histogram of each sub-profile is processed to select dynamic left-limit value and a threshold value to detect Vehicles.  When the window contains an object, the left-limit of the histogram shifts towards the maximum grey value. This process is repeated in 100 frames and the minimum of the left- limit of these frames are selected as the left-limit for the next frames  The left-limit selection program selects a grey value from the histogram of the window, where are approximately zero edge points above this grey value. Histogram containing no object Histogram containing a small part of an object Histogram containing a large part of an object

10. 10 VEHICLE DETECTION ALGORITHM  For threshold selection, the number of edge points greater than the left-limit grey value of each window is extracted for a large number of frames (200 frames) to get enough parameters below and above a proper threshold value.  These numbers are used to create a histogram (horizontal: number of edge points greater than left-limit: vertical: frequency of repetition of these numbers)  Peaks related to the frames passing a vehicle for that frame Number of edge points greater than left-limit Frequency of repetition Before median filter Frequency of repetition Number of edge points greater than left-limit After median filter

11. 11 RESULTS AND DISCUSSION  Operations of the algorithms compared with manual observations of images confirm that the queues are detected and its parameters are measured accurately in real-time.  The average processing speed is about 2 frames per second, enough for real-time.  The program works in such way that after 10s, the presence of the queue and its length is reported  The algorithm is applied to each profile: -If no vehicles are detected repeat the process for this sub-profile again -If vehicles are detected, detection will be applied and the next sub-profile. I no vehicles are detected back to the previous sub-profile.

12. 12 RESULTS AND DISCUSSION  Testing the method under different weather conditions The results show that this queue measurement approach can determine the length of the queue to within 95% accuracy (5% error).

13. 13 CONCLUSIONS  The algorithm uses a new technique by applying a combination of simple but effective operations and has been implemented in real-time.  In order to reduce the computation time, a motion detection operation is applied on all sub-profiles, while the vehicle detection operation is only applied when it is necessary.  The vehicle detection operation uses an edge-based technique which is less sensitive to noise.  The threshold selection for vehicle detection is done dynamically to compensate the effects of variations of lighting and it does not introduce any significant computational cost.

14. 14 CONCLUSIONS  The results show that this queue measurement approach can determine the length of the queue to within 95% accuracy.  This error is mainly due to the objects located very far from the camera and can be reduced by adjusting the size of sub-profiles more appropriately, by analysing camera parameters more accurately.  A practical implementation of this approach called ‘Variable Sign System’, has been operational since early 1995. This system alarms the drivers for heavy traffic, one kilometre before the intersection.

15. 15 BIBLIOGRAPHY HOOSE, N. (1991): ‘Computer Image Processing in Traffic Engineering’. Research Studies Press, Taunton. INIGO, R.M. (1987): ‘Traffic monitoring and control using machine vision: a survey’, IEEE Trans. Indust. Elec., IE-32, (3), pp. 177-185. SIYAL, M.Y., FATHY, M., and DARKIN, C.G. (1994): ‘Image processing algorithms for detecting moving objects’, Proc. of Third International Conference on Automation, Robotics and Computer Vision (ICARCV’94), Singapore. IKRAM, W. (1990): ‘Traffic studies using imaging techniques’. PhD. thesis, UMIST. FATHY, M. (1991): ‘A RISC type programmable morphological image processor’. PhD. thesis, UMIST. HOOSE, N. (1992): ‘Impact: an image analysis tool for motorway surveillance’, Trafic Eng. & Control, pp. 140-147. ROURKE, A., and BELL, M.G.H. (1991): ‘Queue detection and congestion monitoring using image processing’, Traffic Eng. & Control, pp. 412- 421. FATHY, M., SIYAL, M.Y., and DARKIN, C.G. (1994): ‘A low cost approach to real-time morphological edge detection’, Proc. of IEEE TENCON Conference, Singapore. SCHALKOFF, R.J. (1989): ‘Digital Image Processing and Computer Vision’. John Wiley.

16. 16 REAL-TIME IMAGE PROCESSING APPROACH TO MEASURE TRAFFIC QUEUE PARAMETERS. M. Fathy and M.Y. Siyal Conference 1995: Image Processing And Its Applications E N D