Traffic Density Monitoring using OpenCV

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The main objective is a traffic management system where the traffic lights are controlled based on the density of vehicles in each lane and if needed allow provision for priority, on a dynamic allocation method.

Traffic Density Monitoring using OpenCV


The solution we provide for Traffic management is by having a special intelligence which the images of road feed from the cameras (webcam or IP camera) at traffic junctions for real-time traffic density calculation using image processing. It also focuses on the algorithm for switching the traffic lights according to vehicle density on the road, thereby aiming at reducing the traffic congestion on roads which will help lower the number of accidents. In turn, it will provide safe transit for people and reduce fuel consumption and waiting time. It will also provide significant data which will help in future road planning and analysis. In further stages, multiple traffic lights can be synchronized with each other with an aim of even less traffic congestion and free flow of traffic. The vehicles are detected by the system through images instead of using electronic sensors embedded in the pavement. A camera will be placed alongside the traffic light. It will capture images sequences. Image processing is a better technique to control the state change of the traffic light. It shows that it can decrease traffic congestion and avoids the time being wasted by a green light on an empty road. It is also more reliable in estimating vehicle presence because it uses actual traffic images. Traffic Density Monitoring using OpenCV


The number of vehicles on the road is increasing day by day so it is important to manage the traffic flow efficiently in order to utilize the existing road capacity in the best possible way. Traffic congestion is a serious issue nowadays especially in big cities. The main reason behind it is the increase in the population that subsequently increases vehicular travel, which creates congestion problems. This in turn causes an increase in the cost of transportation because of wastage of time and fuel. Traffic congestion is a state on road networks that occurs due to increased usage. It is characterized by slower speeds, longer travel times, and increased traffic blocks (queuing). When vehicles are fully stopped for periods of time, this is colloquially called a traffic jam. Traffic congestion can cause drivers to become frustrated and engage in road rage. In order to avoid congestion in traffic islands, Traffic Signal System (TSS) is used to regulate traffic from different directions and command each lane when to move. An efficient and robust automatic traffic sign detection and recognition can disburden the driver, and thus, significantly increase driving safety and comfort. Generally, traffic signals provide the driver with various information for safe and proper navigation. Earlier it was the sole responsibility of the traffic corps to control the traffic, later manual signal controls were introduced where the traffic corps examine the traffic and control the signals. Then came the automatic recognition of traffic signs, therefore, became important for smooth and tension-free journeys. The present automated traffic control systems work on time-based algorithms. Each lane is allotted a fixed time for the traffic to clear off, the times may be equal for all lanes or based on the average vehicle density

Traffic Density Monitoring using OpenCV


In the existing system, the traffic control is not according to the density and does not reduce the effect of traffic in urban areas. The traffic signals allotted are fixed for some specific time and after a specified time the signal will be changed to another signal. This causes a delay of traffic on other sides of the road for a longer time and in some places, traffic lights also do not work properly which causes traffic congestion. The traffic lights used in India are basically pre-timed wherein the time of each lane to have a green signal is fixed. In a four-lane traffic signal, one lane is given a green signal at a time. Thus, the traffic light allows the vehicles of all lanes to pass in a sequence. So, the traffic can advance in either a straight direction or turn by 90 degrees. So even if the traffic density in a particular lane is the least, it has to wait unnecessarily for a long time and when it gets the green signal it unnecessarily makes other lanes wait for even longer durations. Traffic Density Monitoring using OpenCV


  • That evaluates the traffic density using IR sensors and accomplishes dynamic timing slots with different levels.
  • A portable controller device is designed to unsolved the problem of emergency vehicles stuck in the overcrowded roads
  • The traffic flow depends on the time of the day where the traffic peak hours

Traffic Density Monitoring using OpenCV


Do we propose a technique that can be used for traffic control using image processing.? According to the traffic densities on all roads, our model will allocate smartly the time period of green light for each road. We have chosen image processing for the calculation of traffic density as cameras are very much cheaper than other devices such as sensors.?The proposed model is constructed as follows: We have a Raspberry Pi that is connected to 4 sets of LEDs that represent the traffic lights. It is the process of monitoring the traffic density of each side and? changing the signal according to the density in every direction.


  • It will be density-based which means it will give priority to the lane which has comparatively a greater number of vehicles.
  • Reduce wastage of time and fuel and to saving your time



Traffic Density Monitoring using Raspberry Pi and OpenCV
Traffic Density Monitoring using Raspberry Pi and OpenCV


  • Four cameras are connected with raspberry pi for every direction
  • Lights in each signal are connected to GPIO pins of a Raspberry pi
  • The power supply should be given to raspberry pi
  • Power for the Signal light can be given using external power supply using relay switches (Here we are using LED?s as signal lights)


Each camera should be faced in each direction to monitor the traffic density by having a number of boundaries for each vehicle on the road. When the traffic density in one direction is high, the green signal will turn? in that direction and the rest will be in red. Similarly, by analyzing the order of traffic density traffic signals should be changed in every direction. So that traffic signaling system becomes automatic using image processing through open CV and python.


  • Raspberry Pi
  • Camera-4 (for 4 directions)
  • LED for signals


  • Program: Python
  • Platform: Python 3 IDLE
  • Raspberry pi os: Raspbian os
  • Library: opencv


[1] Somashekhar. G.C, Sarala Shirabadagi, Ravindra S. Hegadi, ? High-Density Traffic Management using Image background subtraction Algorithm?, International Journal of Computer Applications (0975 ? 8887) Recent Advances in Information Technology, 2014

[2] ?ngel Serrano, Cristina Conde, Licesio J. Rodr?guez-Arag?n, ?Computer Vision Application: Real-Time Smart Traffic Light ?,International Journal of Advances in Engineering & Technology, Nov., 2014

[3] Sachin Grover, Vinay Shankar Saxena, Tarun Vatwani, ?Design of intelligent traffic control system using image segmentation? International Journal of Advances in Engineering & Technology, Nov. 2014. ?IJAET ISSN: 22311963

[4] Dr. Swapan Kumar Deb, Rajiv Kumar Nath,?Vehicle detection Based on video for traffic surveillance on road?, International Journal of Computer Science & Emerging Technologies, IJCSET, E-ISSN: 2044- 6004

[5] K.Vidhya, A.Bazila Banu , ?Density Based Traffic Signal System?, International Journal of Innovative Research in Science, Engineering and Technology ,pp 2218-2223,2014

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