A Novel Algorithm for the Duplication Detection and Localization of Moving Objects in Video

We propose a novel algorithm for detecting the duplication of moving objects in video and locating forged motion sequences. First, the algorithm constructs an energy factor ( EF ) curve to identify the suspect frames of the video. Second, an adaptive-parameter-based Visual Background Extractor (ViBe) algorithm (APViBe) is employed for background modelling. Moreover, all motion sequences are identified by using an improved fast compressive tracking (FCT) algorithm based on an adaptive learning rate and measurement matrix (ALMFCT). Third, a similarity-analysis-based scheme (SAS) is designed to search for pairs of suspect motion sequences. Finally, the flip-invariant scale-invariant feature transform (FISIFT) algorithm is used to match the feature points of moving objects in the pairs of suspect motion sequences, based on which the forged motion sequences in the video are confirmed. Experimental results show that the proposed approach outperforms previous algorithms in computational efficiency, accuracy and robustness.