Development of a method for redundant frame removal in video sequences based on assessment of the black-and-white frames similarity
Video sequences redundant frame removal currently plays an important role in various video preprocessing applications related to video classification, change detection, and object recognition (e.g., defects, faults, human actions, etc.). This paper proposes a video sequence subsampling method based on estimating the similarity of two consecutive black-and white frames using a "sign-sign" correlation function and associativity coefficients, verifying the compliance of the calculated metric values with threshold intervals, and making decisions regarding frame removal. A generalized scheme of the proposed method is considered with a detailed description of the stages, and an algorithm for finding similarities and differences between two consecutive black-and-white frames of video sequences is described. This method reduces the size of video sequences by removing redundant or repeated frames, thereby reducing the amount of stored information and increasing the efficiency of detecting key moments in video images obtained in uncontrolled environments. Research results are presented, demonstrating that the proposed method can halve frame redundancy without losing important information or improving the quality of video sequences.