Open AccessAutomatic underwater moving object detection using multi‐feature integration framework in complex backgrounds
Author(s) -
Vasamsetti Srikanth,
Setia Supriya,
Mittal Neerja,
Sardana Harish K.,
Babbar Geetanjali
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0013
Subject(s) - underwater , artificial intelligence , robustness (evolution) , computer science , computer vision , object detection , feature (linguistics) , pixel , pattern recognition (psychology) , frame (networking) , feature extraction , object (grammar) , geography , telecommunications , biochemistry , chemistry , linguistics , philosophy , archaeology , gene
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.