- INTRODUCTION
In an era dominated by technological innovation, the fusion of artificial intelligence and computer vision has catalyzed transformative advancements across various industries. Among these, the domain of transportation stands to benefit significantly from intelligent systems designed to enhance road safety and mitigate the impact of accidents. This project endeavors to contribute to this paradigm shift by presenting a real-time accident detection system, integrating state-of-the-art deep learning and computer vision techniques.
Road accidents remain a pressing global concern, posing a threat to human lives and causing substantial economic losses. Despite advances in vehicle safety features and traffic management systems, there exists a critical need for proactive accident detection mechanisms capable of swiftly identifying potential hazards. This project addresses this need by harnessing the power of artificial intelligence to create a system that can analyze real-time video streams, recognize patterns indicative of accidents, and trigger timely responses.
An Accident Detection System, powered by deep learning and OpenCV (Open Source Computer Vision Library), represents a cutting-edge application at the intersection of artificial intelligence and computer vision. This intelligent system utilizes sophisticated neural networks to analyze real-time visual data captured by cameras or sensors installed on roadways or vehicles. OpenCV, a powerful open-source library, facilitates image processing and computer vision tasks critical for identifying patterns and anomalies in the visual input. The ADS utilizes a combination of cutting-edge technologies, computer vision, and machine learning algorithms, to monitor and identify accidents in real-time. The system operates across various domains, such as traffic management, industrial workplaces, and public safety.
- PROBLEM DEFINITION
In recent years, the integration of cutting-edge technologies such as deep learning and computer vision has revolutionized various domains, including transportation and public safety. One critical application emerging from this technological convergence is the development of real-time accident detection systems. These systems leverage the power of artificial intelligence to enhance road safety by promptly identifying potential accidents and triggering timely responses.
The project at hand focuses on the creation of a real-time accident detection system using a combination of deep learning and computer vision techniques. The core objective is to employ a pre-trained deep neural network to analyze video frames in real-time, identifying instances indicative of road accidents. The implementation utilizes the popular OpenCV library for computer vision tasks and the Keras library for deep learning model integration.
The project addresses a critical challenge in road safety by focusing on the development of a real-time accident detection system. In the contemporary landscape of transportation, traditional accident reporting mechanisms are often plagued by delays, hindering the swift initiation of emergency response efforts. This project aims to overcome this issue through the integration of artificial intelligence and computer vision. The primary problem lies in the timely identification of accidents, especially in scenarios where human observation and manual reporting prove inefficient. Additionally, the system targets the improvement of response times by providing immediate alerts upon the detection of potential accidents. Adapting to diverse environments poses another challenge, as road conditions and lighting can vary significantly.
- EXISTING SYSTEM
The current landscape of road safety and accident detection relies predominantly on manual reporting, surveillance cameras, and GPS-based emergency services. Traditional methods, reliant on eyewitnesses, often lead to delays in incident identification and emergency response.
Traffic surveillance cameras, while widespread in urban areas, may lack real-time automated analysis, particularly in less populated regions. GPS-based systems, while providing automatic alerts, might not address the immediate need for on-the-spot accident detection. Existing solutions also exhibit limitations in adapting to diverse environmental conditions such as varying lighting and weather.
Deep learning models, known for their pattern recognition capabilities, are not widely integrated into current systems, potentially hindering accuracy. Furthermore, managing false positives remains a challenge in many existing systems, impacting the efficiency of emergency response efforts. In light of these limitations, the proposed project aims to fill these gaps by developing a real-time accident detection system that seamlessly integrates artificial intelligence, computer vision, and deep learning technologies.
Here are some potential scopes and implications of the project: Real-Time Accident Detection: The primary scope is the real-time detection of accidents in video streams. This can have significant implications for enhancing road safety by enabling prompt responses to incidents. Video Analytics for Traffic Monitoring: Beyond accident detection, the project has the potential to contribute to broader video analytics applications, such as traffic monitoring and management. The model could be extended to identify and analyze various traffic-related events. Data Collection for Further Research: The project can serve as a valuable source of data for further research in the domain of traffic analysis and accident detection. Collected data could be used to refine models, understand traffic patterns, and contribute to the development of more advanced systems. Machine Learning Model Optimization: The model used for accident detection can be continuously optimized and fine-tuned with additional labeled data. The project scope may involve exploring methods to enhance the model's accuracy and efficiency. Privacy and Ethical Considerations: Considerations related to privacy and ethics are crucial in projects involving video analytics. The project's scope may include implementing measures to address privacy concerns and ethical considerations related to the use of surveillance technology. User Interface and Visualization: Improving the user interface and visualization aspects of the project could be part of the scope. This may involve developing a user-friendly dashboard or interface for monitoring and interacting with the system. It's important to note that the success and impact of the project depend not only on the technical implementation but also on considerations related to user acceptance, regulatory compliance, and the broader context in which the system operates.
10.IMPLEMENTATION 10.1 Code Overview:
Camera.py:
import cv2
from detection import AccidentDetectionModel
import numpy as np
import os
model = AccidentDetectionModel("model.json", 'model_weights.h5')
font = cv2.FONT_HERSHEY_SIMPLEX
def startapplication():
video = cv2.VideoCapture('car.mp4') # for camera use video = cv2.VideoCapture(0)
while True:
ret, frame = video.read()
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
roi = cv2.resize(gray_frame, (250, 250))
pred, prob = model.predict_accident(roi[np.newaxis, :, :])
if(pred == "Accident"):
prob = (round(prob[0][0]*100, 2))
# to beep when alert:
# if(prob > 90):
# os.system("say beep")
cv2.rectangle(frame, (0, 0), (280, 40), (0, 0, 0), -1)
cv2.putText(frame, pred+" "+str(prob), (20, 30), font, 1, (255, 255, 0), 2)
if cv2.waitKey(33) & 0xFF == ord('q'):
return
cv2.imshow('Video', frame)
if __name__ == '__main__':
startapplication()
Detect accident:
from keras.models import model_from_json
import numpy as np
class AccidentDetectionModel(object):
class_nums = ['Accident', "No Accident"]
def __init__(self, model_json_file, model_weights_file):
# load model from JSON file
with open(model_json_file, "r") as json_file:
loaded_model_json = json_file.read()
self.loaded_model = model_from_json(loaded_model_json)
# load weights into the new model
self.loaded_model.load_weights(model_weights_file)
self.loaded_model.make_predict_function()
def predict_accident(self, img):
self.preds = self.loaded_model.predict(img)
return AccidentDetectionModel.class_nums[np.argmax(self.preds)], self.preds
Main.py:
from camera import startapplication
startapplication()
