DENSITY DETERMINATION AND REAL TRAFFIC TIME TRAFFIC CONTROL USING IMAGE PROCESSING

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:02/Issue:09/September-2020

Impact Factor- 5.354

www.irjmets.com

DENSITY DETERMINATION AND REAL TRAFFIC TIME TRAFFIC CONTROL USING IMAGE PROCESSING Pradeep Anand S*1, Jeniksan Benglin G*2, Saravanan S*3 *1Computer

Science and Engineering, Prince Dr K Vasudevan College of Engineering and Technology, Anna University, Chennai, India.

*2Computer

Science and Engineering, Prince Dr K Vasudevan College of Engineering and Technology, Anna University, Chennai, India.

*³Computer

Science and Engineering, Prince Dr K Vasudevan College of Engineering and Technology, Anna University, Chennai, India.

ABSTRACT Vehicle counting is viewed as one of the most significant applications in traffic control and the executives..

Vehicle checking measure gives suitable data about traffic stream, vehicle crash events and traffic top occasions in streets. If we have a good sense of the volume of traffic moving along a given road or network of roads, we can better understand congestion and then manage and/or make plans to reduce/eliminate it. Vehicle count data is very useful to urban city planners and transport authorities. At that point, a little lessening in grouping execution can have genuine monetary misfortunes. Therefore, accuracy and time complexity becomes critical for the traffic system. The algorithm used here will check the quantity of vehicles.. However, it requires large labelled datasets and has restrictions when numerous vehicles are in the scene. Herein, we propose machine learning techniques. The experiments show that our setup is performing as accurately as the existing model with significantly lower labelled datasets We apply Haar Cascade algorithm to determine the number of vehicles in traffic signals. When it is found that the density of vehicles is more in traffic signal it will detect and create an alert.

Keywords: Machine Learning, Image Processing, Traffic Control.

I.

INTRODUCTION

Due to the increasing of road networks and the number of vehicles, traffic monitoring becomes very important. This leads to the quick development of the Intelligent Transportation System (ITS) technologies. Compared to conventional techniques such as microwave detectors, vision-based systems acquire more information about the vehicles. Traffic clog can likewise be brought about by red light delays etc. The traffic signals are pre-programmed. It is independent of the traffic density on the roads [1]. So, the time is wasted on an empty road. The traditional systems are not applicable in the real-time process. One of these technologies is vehicle counting and determining traffic flow. This can be exceptionally helpful for traffic monitoring and guide the drivers for better driving routes through bypassing the congested roads.

In this system, basically, the waiting time for the empty road is reduced. In doing so, the images for each lane are acquired using cameras in sequence and processed [2]. Number of Vehicles present on road and traffic density is calculated by applying appropriate algorithms. Based on the traffic count and density the duration for green light is decided. More time is allocated to the denser road as compared to the empty road. It is not time-dependent. The decision is taken as per the traffic count and density This method provides basic traffic parameters and wide-area detection.

II.

METHODOLOGY

The proposed model will be introduced based on the density of vehicles and predicting the scene class based on the spatial relationships among the vehicles of interest and contextually important objects. This uses machine learning techniques to get a high degree of accuracy from what is called “training data”.

Haar Cascades utilizes the Adaboost learning calculation which chooses few significant highlights from an enormous set to give a productive outcome.

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[675]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:02/Issue:09/September-2020

Impact Factor- 5.354

www.irjmets.com

Haar Cascade Algorithm is an AI object recognition calculation used to distinguish objects in a picture or video. The calculation has four phases: 1. Haar Feature Selection 2. Creating Integral Images 3. Adaboost TTrainin 4. Cascading Classifier Haar-Like Feature Haar-Like is a rectangular straightforward element that is utilized as an information include for the fell classifier. By applying all of these channels into one extraordinary territory of the picture, the pixel aggregates under white regions are deducted from the pixel wholes under the dark regions. That is the heaviness of white and dark territory can be considered as “1” and “-1”, respectively.

AdaBoost Algorithm AdaBoost calculation (an AI meta-calculation) in picking highlights and improving the exhibition is consistently utilized. AdaBoost, so as to build a solid classifier, consolidated numerous feeble classifiers.

AdaBoost is the way that blends a progression of AdaBoost classifiers as a channel chain.

Each channel is a different AdaBoost classifier which comprises of a couple of feeble classifiers.. If every one of these channels in the acknowledgment district of the picture shows the vehicle falls flat, this region is quickly named a non-vehicle. At the point when a channel acknowledges a territory of the picture as a vehicle, the zone enters the following channel in the chain. In the event that this zone of the picture passes all the chain channels effectively, it is delegated a vehicle. In this calculation, each pattern of boosting a

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[676]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:02/Issue:09/September-2020

Impact Factor- 5.354

www.irjmets.com

component among all other potential highlights is chosen and at long last, the last order will be straight joins of the underlying powerless characterization.

Integral Image Integral picture is a snappy technique for figuring the Haar-Like component. Has utilized this strategy and they perceived which Haar-Like element among the other picture. Basic picture is the total of all pixel values. With integral image Haar-Like feature can be calculated quickly by simple addition and subtraction. Integral images can be characterized as two-dimensional query tables as a lattice with a similar size of the first picture.

Cascaded Classifier The cascaded classifier is utilized for quick dismissal of mistake windows and improving the preparing speed. In every node of trees, there is a non-vehicle expanding, it implies that the picture won’t be the vehicle. By this method, the bogus negative rate is in any event. The course classifier comprises of an assortment of stages, where each stage is a troupe of frail students. In the event that the name is positive, the classifier passes the area to the following stage. The detector reports an article found at the current window area when the last stage orders the district as certain.

III.

RESULTS AND DISCUSSION

The figure given beneath shows the yield of the given utilized calculation and its execution.

Expecting day time examination, four previews of every one of the four headings is caught, with the assistance of high-goal cameras, when there is the unimportant thickness of vehicles out and about. These four pictures go about as a kind of perspective. For the rest of the aspect of the day, the pictures are caught at a customary time period seconds and afterward contrasted and the reference pictures utilizing picture handling procedures portrayed through pictures underneath.

Fig (a) speaks to the reference picture one way of a four-way crossing point. Fig (b) is the continuous picture which is caught at a customary stretch. Now both, fig (an) and fig (b) are changed over into greyscale as appeared in fig (c) and fig (d) separately. Presently for assessment of ongoing traffic thickness, we take away fig (c) from fig (d) Using deduct order, which brings about fig â‚Ź. Presently as it needs to characterize the limit levels for computerized signal handling, we use graythresh order and for

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[677]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:02/Issue:09/September-2020

Impact Factor- 5.354

www.irjmets.com

better differentiability fig € is changed over into a paired picture utilizing im2bw order as appeared in fig (f). In any case, to eliminate the low-thickness zones, for example, winged animals, flags, streetlights and so forth., a channel is required for improved outcomes. This sifting cycle is appeared in fig (g). The white part on the picture speaks to the traffic thickness, so the level of the white bit is determined and its worth is sent to the regulator for additional estimations and clock control.

IV.

CONCLUSION

An efficient density based traffic control system is simulated and implemented which provides a good traffic control mechanism without time wastage. It is also a much better way of detecting the presence of vehicles on the road since it makes use of image data. So it surely operates much better than systems which rely on the metal content of the vehicles to detect their presence. Image processing techniques beat the impediments of all the traditional methods of traffic control. It eliminates the need for extra hardware and sensors. The use of multiple cameras will help to analyze and control traffic in a particular region.

The proposed framework beats the current framework as far as precision and effortlessness.

The weather conditions are not taken into account which may affect the image quality when it becomes foggy or in heavy rains. More advancements can be made to the proposed system to check the identification of vehicles that pass through the system circle which could help in traffic surveillance.

ACKNOWLEDGEMENTS The authors wish to thank Prof. Deepa N and Prof. GowriVidhya N for their consistent support and guidance for this research project under P. D. K. V, Anna University.

V. [1] [2]

[3]

REFERENCES

Mohamed A. Abdelwahab, “Accurate Vehicle Counting Approach Based on Deep Neural Networks”, International Conference on Innovative Trends in Computer Engineering (ITCE) Muhammad Brilliant Subaweh: Eri Prasetyo Wibowo, “Implementation of Pixel Based Adaptive Segmenter method for tracking and counting vehicles in visual surveillance”, International Conference on Informatics and Computing (ICIC) Qiaoqian Chen; Na Huang; Jieming Zhou; Zhao Tan, “An SSD Algorithm Based on Vehicle Counting Method”, 37th Chinese Control Conference (CCC).

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[678]