Abstract:
The study of computer vision makes it possible for machines to replicate the visual system of humans. It is a type of artificial intelligence that gathers data from movies or digital photos and manipulates them to specify the properties. Throughout the process, images are acquired, screened, analyzed, and identified as the information is extracted. Computers can recognize and respond appropriately to visual input thanks to this sophisticated processing. This study's major objective is to utilize computer vision to identify traffic accidents, as these incidents frequently result in fatalities. One of Bangladesh's key problems is the country's high rate of traffic accidents, which is a worldwide problem that impacts more than just Bangladesh. The majority of traffic accidents happen abruptly and without warning, having a major negative impact on both human activity and traffic flow. It is vital to identify traffic incidents as soon as possible and alert oncoming motorists since in the majority of instances, subsequent accidents may be prevented if only prompt identification and prompt rescue were permitted. The organizations' lack of coordination and the careless driving that goes along with it are the causes of these circumstances. Sadly, the primary cause of the high fatality rate in road accidents is the delayed delivery of emergency assistance to accident victims. An accident sufferer may be left neglected for an extended period on highways with light and quick traffic, which might lead to the deaths of those involved in the accident. This paper employs computer vision in detecting accidents from image frames of CCTV footage. The primary goal of this work is to categorize video frames into accident and non-accident categories by training a deep-learning convolution neural network model using each frame of the movie. Convolutional Neural Networks are a quick and accurate method of categorizing photos; with comparably smaller datasets, CNN-based image classifiers have demonstrated an accuracy of over 89%.also discusses the challenges, risks and future uses of ChatGPT and Gemini for ensuring applicable outcomes in education sector.
