Affiliations: [a] Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Anna University, Chennai | [b] Department of Computer Science and Engineering, St. Xavier’s Catholic College of Engineering, Anna University, Chennai
Correspondence:
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Corresponding author. D. Minola Davids, Assistant Professor, Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Anna University, Chennai. E-mail: [email protected].
Abstract: The visual data attained from surveillance single-camera or multi-view camera networks is exponentially increasing every day. Identifying the important shots in the presented video which faithfully signify the original video is the major task in video summarization. For executing efficient video summarization of the surveillance systems, optimization algorithm like LFOB-COA is proposed in this paper. Data collection, pre-processing, deep feature extraction (FE), shot segmentation JSFCM, classification using Rectified Linear Unit activated BLSTM, and LFOB-COA are the proposed method’s five steps. Finally a post-processing step is utilized. For recognizing the proposed method’s effectiveness, the results are then contrasted with the existent methods.
Keywords: Video summarization, Levy Flight (LF) and opposition-based learning, Coyote Optimization Algorithm (LFOB-COA), Bi-directional Long Short-term Memory (BLSTM), Jaccard Similarity-centered Fuzzy C-Means (JSFCM)
