Semantic Video Mining for Accident Detection

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