REAL TIME EMERGENCY RESPONSE SYSTEM AND TRAFFIC MANAGEMENT

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN:2395-0072

REAL TIME EMERGENCY RESPONSE SYSTEM AND TRAFFIC MANAGEMENT

G. Hima Varsha1a, B. Nikhil1b, P. Yashwanth Sai Karthikeya1c, k.Tharuni1d, Mrs. D. Padma2 , K. Somasekhar3

1a-dB.Tech Students, Department of Computer Science and Engineering (AI-ML), Dadi Institute of Engineering and Technology, Anakapalle, Andhra Pradesh, India.

2-3Assistant Professor, Department of Computer Science and Engineering (AI-ML), Dadi Institute of Engineering and Technology, Anakapalle, Andhra Pradesh, India. ***

Abstract-Real time emergency response and traffic prevention is important for the public safety and traffic management. The system in this project introduce the real time emergency vehicle detection and traffic prevention model using OpenCV. OpenCV is a powerful computer vision library. The system is particularly designed to identify the emergency vehicles like ambulance, fire trucks and police vehicles in real time fromvideostreamsorfeeds.Objectdetectionalgorithms are used with OpenCV's image processing capabilities then the system can detect the emergency vehicle pattern,identifiestheirpresenceandinitiateresponsein theform of trafficsignal adjustmentorother preventive measures towards smooth passage for emergency vehicles. The emergency vehicles are detected by color detection method, shape detection, contour analysis and matchfeatures.Oncethe emergencyvehicleisidentified then the system can send signals to the traffic control system to alter traffic patterns, thereby reduce delays and improving response times for emergency services. Theproposedsystemdemonstratesareal-time,accurate, andefficientsolutiontoemergencyvehicledetectionand trafficcongestionmanagement.

KEYWORD: Emergency vehicle detection, Traffic prevention, OpenCV, Image processing capabilities, Colour detection, Shape detection, Contour analysis, Traffic management, Public safety, Computer Vision.

1.INTRODUCTION

The traffic in urban areas are widely increasing day by day, so the emergency vehicles like ambulance, fire trucks, police vehicles are getting delay, which leads to severe consequences including loss of lives. By recognizing the problem the real time emergency response system works on the emergency vehicle identificationandcontrolthetrafficontheroad.

The system uses the powerful computer vision library likeOpenCVtodetecttheemergencyvehicleinrealtime fromvideostreamorcamerafeeds.Byusingtheadvance object detection algorithm and image processing

techniquesthesystemisdesignedtoidentifyemergency vehiclebasedonspecificpattern,colorandshape.

The system uses the color and shape detection methods to identify the emergency vehicle. By the counter analysismethod,itenhance theaccuracyofdetectionby isolating emergency vehicle shape from background noise and other distractions in urban environment. This is helpful for the system to identify the emergency vehicle in different conditions, including low light and hightrafficdensity.

Once the emergency vehicle is detected the traffic control systemstartsworkingbyclearingthetrafficand managing the traffic light signals. The response mechanism allows the real time action to ensure that emergency vehicles are able to reach the destination withoutanydelay.

1.1 ARCHITECTURE

The architecture of real time emergency response systemandtrafficmanagementisdesignedtocontrolthe traffic system in emergency conditions and ensures the emergencyvehiclereachthedestinationwithoutdelay.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN:2395-0072

2.LITERATURE SURVEY

1. Emergency Vehicle Detection by Computer Vision: Many works have used computer vision methods to detect emergency vehicles. For example, in Xia et al. (2018), an object detection method is proposed to recognize emergency vehicles on the road through realtimevideodata.Inthiswork,featureslikeflashinglights, shape, and size of the vehicle were used to detect the objects.Thisresearchworkshowshowimageprocessing may be utilized to accurately detect the vehicle, yet lightingconditionsandocclusionspersistasproblems.

2. Traffic Management Systems and Priority Control: Traffic congestion control, in part, involves automatic traffic light response to the passing of an emergency vehicle. Tawari and Bhattacharya (2017) proposed a system in which traffic signal systems are adjusted according to the detection of an emergency vehicle.Thesystemdynamicallyadjustedtrafficlightsin real-time using sensors such as GPS and inductive loop sensors, thus reducing waiting time for emergency

vehicles. Sensor-based systems can be expensive and requirefrequentmaintenance.

3. Deep Learning and Machine Learning for Object Detection: Very recent studies use deep learning-based approaches for the detection of vehicles. Zhao et al. (2019) have used convolutional neural networks for the detectionofvehiclesincomplexenvironments.CNNsare veryeffectivefordistinguishingemergencyvehiclesfrom other vehicles as they learn spatial hierarchies of features. However, these models need large labeled datasetsandalotofcomputationalresources.

4. Feature Matching and Shape Recognition:

Traditionalmethodsofimageprocessingincludefeature matching, shape recognition, and color detection. In the literature,Ranietal.(2018)usedcolorsegmentationand shape recognition algorithms to detect emergency vehicles. These methods are computationally efficient and do not require a large amount of data but may face challenges in distinguishing emergency vehicles under variablelightingandenvironmentalconditions.

5. Computer Vision with Traffic Signal Systems: One of the most promising research areas is integrating computer vision techniques with existing traffic signal systems for real-time traffic management. In Gopalan and Reddy (2020), a traffic control system was developed using machine learning algorithms to detect emergency vehicles and adjust the green signal for their passage. This system dynamically managed traffic lights toreducetheresponsetimeinemergencyscenarios.

6. Challenges and Limitations: Even though there has beenprogressinemergencyvehicledetectionandtraffic management, challenges are still present in the implementation of real-time systems. Environmental conditions such as light variation, weather, and occlusions (for example, parked cars, pedestrians) can influencetheaccuracyofdetectionalgorithms.According to Vasquez et al. (2016), one of the major limitations is the requirement for robust algorithms that can function effectively in different urban environments. In addition, such systems are very complex and, therefore, involve a significant hardware infrastructure that renders their implementationcost-prohibitiveandnoteasytoscale.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN:2395-0072

2.1 EXISTING SYSTEM

Traditionalemergencyvehicledetectionmethodslargely depend on manual observation that lack the sophistication needed for real-time, automated analysis. This older system often rely on basic sensors, static cameras, or human intervention to identify emergency vehicles.

2.1.1.Limited real-time detection: Many existing systemsarenotcapableofprocessinglivevideostreams in real-time, leading to delays in detecting emergency vehicles and triggering necessary actions, such as traffic signal adjustments. Traditional systems often rely on frame-by-frame analysis that is too slow to handle fastmovingemergencyvehiclesindynamictrafficscenarios.

2.1.2.Poor adaptability: Traditional emergency vehicle detection methods are often limited in their ability to adapt to diverse traffic conditions and different types of emergency vehicles. These systems may struggle to differentiate between various emergency vehicles (e.g., ambulances, fire trucks, police vehicles) and regular vehicles under different environmental conditions such aspoorlighting,adverseweather,orobstructedviews.

2.1.3.High false positives:Manyexistingsystemssuffer fromahighrateoffalsepositives,wherenon-emergency vehiclesareincorrectlyidentifiedasemergencyvehicles. This can happen due to similar characteristics, such as flashing lights on non-emergency vehicles or other factors like reflections or motion patterns in traffic. The presence of false positives reduces the system's reliability and may cause unnecessary traffic signal changes, leading to disruptions for non-emergency vehiclesandoveralltrafficinefficiency.

2.1.4.Lack of integration: Akeylimitationoftraditional methodsistheabsenceofintegrationwithtrafficcontrol systems.Mostdetectionsystemsoperateinisolationand donotcommunicatewithtrafficsignalinfrastructure.

2.2 PROPOSED SYSTEM

This emergency vehicle detection and traffic prevention system uses advanced OpenCV-based image processing techniques for real-time emergency vehicle detection to implementthepreventionoftrafficthroughmechanisms favoringthefreeflowofthesevehicles.

2.2.1.Real time detection: Thesystemtrackslivevideo streams or feeds from cameras and detects emergency vehicles. The system uses real-time video processing techniques to recognize immediate traffic flow and identify any emergency vehicles. Utilizing OpenCV, the systemcanprocesshigh-speedvideostreamswithspeed fortimelydetection.

2.2.2Emergency vehicle recognition: Thereareseveral imageprocessingtechniquesemployedby thesystemto differentiate emergency vehicles from the rest of the regulartraffic.Theseincludecolordetectionsuchasthat ofredor bluelightsonafiretruckorpolicecar.Also,to recognize shapes such as fire trucks or ambulances, and finally contour analysis. These enable the system to distinguishemergencyvehicleswithhighreliabilityeven inbusytrafficenvironments.

2.2.3.Traffic light control: After detecting an emergency vehicle, the system automatically communicates with traffic light control mechanisms and changes signals instantaneously. The whole traffic sequence can be changed by the system. Emergency vehiclescouldpassintersectionswithminimaldelaydue toredlightsbecausethesystemensuresthattheygetthe right of way, efficiently clearing the way without anyone'sinterference

.2.2.4.High accuracy: The proposed system combines multiple image processing techniques to minimize false positives. By using a combination of shape detection, pattern recognition, and color analysis, the system ensures that only emergency vehicles are identified and thatregularvehiclesarenotmistakenlyprioritized.This increases detection accuracy and reduces unnecessary traffic signal changes, preventing disruptions for nonemergencyvehicles.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN:2395-0072

2.2.5.Integration with traffic systems: The system is designed to integrate well with existing traffic control infrastructure.Itcommunicatesdirectlywithtrafficlight systems, dynamically adjusting the flow of traffic based on real-time data. This integration ensures that the trafficmanagementsystemisresponsivetothepresence of emergency vehicles and can quickly implement measures to improve their passage through intersections.

2.2.6.Scalability: Thisisasystembuilttoscaleforhighresolution video feeds and multi-camera streams. It can, therefore,managedifferenttrafficconditionsindifferent cities,processingcamerafeedsfromdifferentcamerasat once. The high scalability enables a wider range of applications, whereby the system would be able to handle traffic easily in a metropolitan city or over complexintersections.

3.IMPLEMENTATION

3.1.Import libraries

1.importcv2

cv2:ThisistheOpenCVlibraryforimageprocessing.

2.importnumpyasnp

numpy: A library for handling arrays and numerical computations.

3.2.Data preprocessing

Forpreprocessing, theImageDataGenerator isused.It is usedforthegenerationofbatchescontainingthedataof tensorimagesandisusedinthedomainofreal-timedata augmentation.

3.3.Model training

Load and examine the train.csv and test.csv datasets. Identify and note important features useful for model training.

("train.csv”,"test.csv")

Read the dataset and train the model from train.csv and test.csv datasets. Upto 30 epoches were taken to initiate the model, the epochs and loss graph is given as the output.

Fig-4 Representation of epochs and loss

3.4.Output prediction

The output could be detecting the vehicle whether it is emergencyvehicleornot.

3.5.System workflow

1. Thesystemcontinuouslymonitorstrafficvialive videofeeds.

2. OpenCV processes the video data, detecting vehiclesbasedontheircolor,shape,andpattern.

3. When an emergency vehicle is detected, the system triggers traffic control systems to adjust signals.

4. Traffic lights are switched to allow the emergency vehicle to pass without delay, improvingresponsetimes.

5. The system maintains high accuracy by distinguishing between emergency vehicles and regular traffic, ensuring that false positives are minimized.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

3.6.Result Fig-5 Final output

4.CONCLUSION

The Real Time Emergency Response System and Traffic Management developed in this project demonstrates a significant leap forward in optimizing urban traffic management for emergency situations. By utilizing OpenCV and image processing techniques, the system successfully identifies emergency vehicles such as ambulances,firetrucks,andpolicecarsinreal-timefrom video streams. The system then triggers automatic adjustments in traffic light signals to facilitate the swift passage of emergency vehicles, reducing delays and enhancingtheefficiencyofemergencyresponsetimes.

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