AN APPLICATION OF SPOTTING OF UNEXPECTED ACCIDENT UNDER BAD CCTV MONITORING CONDITIONS IN DANGEROUS AREAS USING DEEP LEARNING CONVOLUTIONAL NUERAL NETWORK ALEX NET (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2023)
As the urban population rises and the number of motor vehicles increases, traffic pollution is becoming a major concern in the twenty-first century. Accidents are a major cause of traffic delays since they not only result in injuries and losses for those involved, but also in lost and squandered time for others who are stuck behind the wheel. The proposed Object Detection and Tracking Technology (ODTS) would be used and expanded to automatically identify and control irregular events on CCTVs in tunnels in conjunction with a well-known deep learning network, Faster Regional Convolution Neural (Faster R- CNN), for Object Detection and Traditional Object Tracking. It enables the detection of a moving target in real time, which is typically not possible in standard object tracking systems. The proposed method takes a time frame as input for Object Detection Bounding Box discoveries, comparing current and preceding picture bounding boxes to provide a unique ID number to each moving and detecting object. [3] A video clip is the suggested system. It enables the detection of a moving target in real time, which is typically not possible in standard object tracking systems. As a result, the computer will identify any and all injuries. More specifically, because the training data set is large, it is possible to automatically improve the ODTS capabilities without modifying the programme codes