Volume 5, Issue 6, June – 2020 International Journal of Innovative Science and Research Technology ISSN No:-2456-2165 IJISRT20JUN432 www.ijisrt.com 670 Semantic Video Mining for Accident Detection Rohith G Dept. of Computer Science Sahrdaya College of Engineering and Technology Thrissur, India Twinkle Roy Dept. of Computer Science Sahrdaya College of Engineering and Technology Thrissur, India Vishnu Narayan V Dept. of Computer Science Sahrdaya College of Engineering and Technology Thrissur, India Shery Shaju Dept. of Computer Science Sahrdaya College of Engineering and Technology Thrissur, India Ann Rija Paul Assistant Prof. (Dept. of Computer Science) Sahrdaya College of Engineering and Technology Thrissur, India Abstract:- This paper depicts the efficient use of CCTV for traffic monitoring and accident detection. The system which is designed has the capability to classify the accident and can give alerts when necessary. Nowadays we have CCTVs on most of the roads, but its capabilities are being underused. There also doesn’t exist an efficient system to detect and classify accidents in real time. So many deaths occur because of undetected accidents. It is difficult to detect accidents in remote places and at night. The proposed system can identify and classify accidents as major and minor. It can automatically alert the authorities if it deals with a major accident. Using this system the response time on accident can be decreased by processing the visuals of CCTV. In this system different image processing and machine learning techniques are used. The dataset for training is extracted from the visuals of already occurred accidents. Accidents mainly occur because of careless driving, alcohol consumption and over speeding. Another main cause of death due to accidents are the delay in reporting accidents since there doesn’t exist any automated systems. Accidents are mainly reported by the public or by traffic authorities. We can save many lives by detecting and reporting the accident quickly. In this system live video is captured from the CCTV’s and it is processed to detect accidents. In this system the YOLOV3 algorithm is used for object detection. Nowadays traffic monitoring has a greater significance. CCTV’s can be used to detect accidents since it is present in most of the roads. It is only used for traffic monitoring. Normally accidents can be classified as two classes major and minor. The proposed system is able to classify the accident as major or minor by object detection and tracking methodologies. Every accident doesn’t need emergency support. Only major accidents must be handled quickly. The proposed system captures the video and undergo object detection algorithms to identify the different objects like vehicles and people. After the detection phase the system will try to extract the features of the vehicles. The features like length, width and centroid are extracted to classify the vehicle accordingly. The vehicle count is also detected, which can be used for traffic congestion control. Keywords:- YOLO V3, SSD , Faster RCNN , RCNN. I. INTRODUCTION Population is increasing day by day. Along with the increase in population, the number of vehicles are also increasing. It is known that the present traffic management system is not efficient. Millions of people die in road accidents every year. This is not only because of the increase in the number of vehicles. There doesn’t exist any proper system to detect accidents and to alert the authorities. The higher response time for the arrival of the emergency system causes many precious lives. Normally the road accidents are reported by the people near the accident. Many of the cases the people who witness the accident are not willing to alert the authorities and instead they are busy taking selfies. These types of negligence are causing precious lives. Also we have CCTVs installed on most of the roads. But the CCTV’s are not used efficiently. In the modern era where the technology is growing faster we are still dependent on human power for traffic monitoring.Since the number of traffic authorities is low and the number of vehicle users is high it is difficult to control them. Many people lose their life because of undetected accidents. It is difficult to monitor vehicles all the time for humans. But it is easy and possible by using CCTV’s. The proposed system uses CCTV for traffic monitoring and accident detection with less human interventions. The system captures live video from CCTV and processes it to detect accidents in real time. Surveillance cameras are installed in most of the roads. This is mounted on a pole which can give clear vision of vehicles on the road. Present system uses these visuals to monitor and control the traffic manually.