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access icon free Automatic underwater moving object detection using multi-feature integration framework in complex backgrounds

© The Institution of Engineering and Technology
Received 18/01/2017, Accepted 20/03/2018, Revised 14/12/2017, Published 21/03/2018

Moving object detection in a video sequence is one of the leading tasks of marine scientists to explore and monitor applications. The videos acquired in the underwater environment are usually degraded due to the physical properties of water medium as compared with images acquired in the air and that affects the performance of feature descriptors. In this study, a new feature descriptor, multi-frame triplet pattern (MFTP) is proposed for underwater moving object detection. The MFTP encodes the structure of local region based on three sets of frames, which are calculated by considering local differences in intensities between the centre pixel and its nine neighbours. Furthermore, the robustness of the proposed method is increased by integrating it with colour and motion features. The performance of the proposed framework is tested by conducting seven experiments on Fish4Knowledge database for underwater moving object detection applications. The results of the proposed method show a significant improvement as compared with state-of-the-art techniques in terms of their evaluation measures.

Inspec keywords: image segmentation; object detection; image motion analysis; video signal processing; image sequences; feature extraction

Other keywords: leading tasks; object detection applications; multiframe triplet pattern; videos; underwater environment; video sequence; complex backgrounds; integration framework; marine scientists; motion features; colour; MFTP; feature descriptor

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing

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