Multi-feature joint descriptor based image detection algorithm for crocodile detection

Travelers, sailors and research people working in sea environment are facing lot of challenges. One such challenge is salt water crocodile. In general, crocodiles are seen in fresh water like lake and river. Nowadays it is migrated to salt water. These salt water crocodiles are serious threat to travelers and sailors. This issue is addressed through solution like Ocean Observation System (OOS), which integrates video frame system with target detection. Development of OOS is a challenging task due to various image processing modules like background occlusion, object deformation, scale changes, etc. Detection algorithms using global feature descriptor such as colour, failed to produce reliable detection, when the background image has similar colour. In case of motion pictures, reliability of detection becomes less while using local feature descriptors like Histogram of Oriented Gradient (HOG), Scale-Invariant Feature Transform (SIFT) etc., As target detection through such single feature representation does not produce reliable results, we propose a Multi-Feature Joint Descriptor (MFJD) method in this paper. An optimization of crocodile detection process is obtained through the implementation of two different MFJD and by comparing their performance on detection rate. The feature descriptors considered in the proposed MFJD algorithms includes Colour, HOG and SIFT. The simulated results ensure the reliability of detection achieved through MFJD detection process.