Visual tracking system for arapaima gigas in underwater environment

Abstract

Invasive Alien Species (IAS) have recently become issue of concern, due to their adverse ecological effect. Among IAS, the fish sub-class constitute a major problem to ecological balance of inland water bodies in Malaysia. Method of containment mostly employed to invasive fish without harming indigenous fish species would involve direct human effort. The involvement of humans in physical and direct containment of invasive fish species can be very tedious, as it involves diving and hunting of alien fish species. However, the use of vision based underwater robots can greatly reduce the cost, effort and risk involved, as well as yield more result in shorter time. Underwater robot vision system is primarily built upon visual recognition and tracking. In this study, the particle filter tracking algorithm is employed for underwater tracking of Arapaima Gigas, where modifications for its improvement were proposed. The improvement is towards enhancing the tracker performance in terms of accuracy and tracking error. Two classes of improvements were proposed, namely multi-likelihood and fused-feature matching. The features used for tracking are namely Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Fast Accelerated Segment Test (FAST). The result from fused-feature SURF-FAST tracker was the best in terms of performance indices, namely accuracy and tracking error.