Design of a Video Summarization Scheme in the Wavelet Domain Using Statistical Feature Extraction

The marine researchers analyze the behaviors of fish in the sea by manually viewing the full video for their research activity. Searching events of interest from a video database is a time consuming and tedious process. Video summary refers to representing the whole video using few frames. The objective of this work is to design and develop a statistical video summarization to perform the automatic detection of events of interest in underwater video. In this proposed work, a video is partitioned into adjacent and non-overlapping datacubes. Then, the video frames are transformed into wavelet sub-bands and the standard deviation between two consecutive frames is computed. Pixels of interest in frames are identified using threshold values. Key frames are identified using Local Maxima and Local Minima. The proposed work effectively detects even the movement of small water bodies such as crabs which is not detected using the existing methods. Finally, this paper presents the experimental results of proposed method and existing methods in terms of metrics that measure the valid of the work.