Key frame extraction based abnormal vehicle identification technique using statistical distribution analysis

In recent days, due to potential growth of vehicle usage, the researchers have to concentrate on abnormal vehicle identification areas to provide solutions to avoid accidents. Though many vehicle identification works have been done by applying machine and deep learning approaches, still there is some problem with handling repetition frames and identifying the abnormal vehicles among vehicles in a camera. To overcome these challenges, this paper introduces KFEAVI (Key Frame Extraction based Abnormal Vehicle Identification) technique that uses statistical feature extraction technique and constrained angular second moment technique. The statistical feature extraction technique is used to extract the key frames in a statistical way by using beta distribution estimation. This technique handles well for both gradual and abrupt content changes in frames. The constrained angular second moment method is applied to find the vehicles to identify the abnormal vehicles movement. The experimental results are carried out using Car Accident Detection Dataset (CADP). For evaluating performance of KFEAVI, several algorithms are compared with the KFEAVI. The experimental results reveal that the KFEAVI achieved better results in terms of F-score.