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Smart Control of Traffic Signal System using Image Processing
- 1. Smart Control of Traffic Signal System using Image Processing PRESENTATION ON EE4130 Prepared by: Raihan Bin Mofidul Roll:1103021 TECHNICAL SEMINAR ON 1
- 2. INTRODUCTION Objectives: This paper focus on the necessity of intelligent traffic system and the peculiar way of Implementation with embedded system tools. Here it is implemented using an object counting methods and detection of emergency vehicles simultaneously thereby control the traffic signals based on the priority outcome. Outcomes: Accuracy of this work can be improvised further by doing thermal image processing. Thermal image processing is effective even during extreme weather conditions such as, mist or fog. Secondly, Cloud computing can be done for the road data analysis Source: Published in: Indian Journal of Science and Technology ,Vol 8(16), 64622, July 2015 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 DOI:10.17485/ijst/2015/v8i16/64622 IJST Scopus Indexed 2
- 3. AGENDA: a) Proposed Procedure b) Project Overview c) Required Hardware and software models d) Detection of ambulance & Vehicles e) Traffic Density Estimation f) Graphical representation g) Result & conclusion 3
- 4. PROPOSED PROCEDURE The frames of the traffics obtained from the surveillance camera through continuous video processing. To calculate the density, an image from the camera is used to calculate the number of vehicles in each lane. According to the number of vehicles in each lane, the time for respective green signal is given which varies time to time. If there are same numbers of vehicles in the lane, the signal will follow the basic timer circuit. If an ambulance is detected , the counter display will show an ambulance symbol, after a few seconds the lane having the ambulance will be allowed. Incase if there are two ambulance detected in the junction, the ambulance which is nearer to the signal get the priority first. 4
- 5. PROJECT OVERVIEW: PC {Image Processing}Start Camera Data of ambulance and traffic density Ambulance ? YesNo Traffic Density Polarizing the lanes according to the Traffic Density Number of Ambulance One ? No Polarizing the lanes according to the Ambulance Density Its Distance Selection of lane Yes MCU to toggle Traffic signals 5
- 6. REQUIRED HARDWARE AND SOFTWARE MODELS: Hardware Models: I. USB web camera: To capture images II. PC: For all the image processing work, III. MCU: Arduino board for signal prioritizing, Software Models: I. The density and ambulance detection is done using MATLAB only for the prototype modeling circuit. II. The traffic signal is controlled by using Arduino microcontroller. 6
- 7. DETECTION OF AMBULANCE & VEHICLES: Detection of Ambulance : I. The red color & blue color of the siren in the image is obtained using image segmentation. II. If the distance between red & blue pair is lesser than a given predefined threshold distance and if both the centroids lie on the same vehicle then the corresponding red & blue color is coming from the siren and the vehicle is found to be an ambulance. Detection of Vehicles: I. If above conditions are not valid , then they are considered to be normal vehicles 7
- 8. TRAFFIC DENSITY ESTIMATION: 8 I. To obtain the number of vehicles on the road, the current frame and the Background frame are converted to gray scale. II. The images are compared and subtracted to obtain presence of objects on the road. III. This image is further enhanced and it is converted to binary image. This image is then filtered using Gaussian filter and to obtain only the vehicles on the road. IV. Count of Vehicles: The next step is to count the number of vehicles present on the roads. To achieve these ,we’ll use sets of connecting pixels in gray scale .
- 9. GRAPHICAL REPRESENTATION: 9 Figure 1 : (a) Reference Frame, (b) Current Frame, (c) Current frame with ambulance Figure 2 : (a) Cropping of background frame, (b) Cropping of current frame 1, (c) Cropping of current frame 2. Figure 3 finding ambulance: (a) Cropped Image of Lane 1, (b) Red Color, (c) Binary image of Red objects, (d) Blue Color, (e) Binary image of blue object Figure 4 Measuring traffic density :(a) Image after background subtraction (Grayscale), (b) Image after Enhancement (Binary Image). Figure 5 :Estimation of the practical preview of smart traffic control procedure using Image processing.
- 10. RESULT & CONCLUSION: 10 In this project they had successfully made the prototype for real time image processing for smart automation of traffic signal system for density estimation and emergency vehicle detection such as ambulance. This model detects the ambulance by detecting its siren. This is achieved through image segmentation based on red & blue color of the siren. The traffic density on each lane is also estimated and the traffic signal is prioritized accordingly. Cameras can be used for other purposes such as security & videos storing etc.
- 11. 11 THANKS TO ALL شكراللجميع QUESTIONS & ANSWER: