SUZE: A Hybrid Approach for Multi-fish Detection and Tracking

Development of deep sea observatories has led to increased demand of automatic fish detection and tracking. Estimating fish number and their track can help the biologists to understand underwater environment and nature of marine animals. The paper defines the current state of the research in automatic fish detection and tracking systems. The paper discussed the steps involved in the process, the challenges encountered, and different ways to proceed in the direction. Feature extraction algorithms plays a vital role in object detection and tracking. Numerous work has been done using distinct feature extraction algorithms like Gabor, SIFT for detecting, and CamShift, Kalman Filter for tracking fishes as well. This paper proposes an advanced approach by contemplating SURF with KAZE, which can be employed for not only getting feature set but also to characterize a particular object. SIFT though the most accurate feature descriptor, is comparatively slow and hence costs time by virtue of mathematical complexity. Researchers needed more speeded up version of SIFT. When Herbert Bay et al., introduced SURF (Speeded–up Robust Technique), which was perfectly a speeded–up version of SIFT, a scale and rotation invariant algorithm, the employability of SURF came in demand. However, because of the practice of Gaussian scale space, SURF tends to overlook the natural borders of the object as it treats noise and details to the same level, reducing inhabitant accuracy and peculiarness. KAZE on the other hand, by usage of non-linear scale space with the aid of non-linear dispersion filtering, gravitate to the localization points and borders maintaining the characteristic features of the object. Appropriate incorporation of these feature extraction techniques to produce a feature set that can produce distinctive characterization of the object is manifested in the paper. The feature set produced using this novel technique when incorporated with support vector machine produces an efficient tool for detection and classification of fishes. The paper have demonstrated the use of this detection tool for calculating the trajectories of objects by making use of centroid based tracking. The paper demonstrates a comprehensive evaluation on standard datasets available with respect to fishes and also the testimony of feature calculation using the novel method on poised data. One major constraint in underwater fish detection i.e. occlusion, is also referred and is taken care of with the help of interpolation. Due to the clamorous nature of underwater scenario (underwater images are correlated with illumination of light and the electromagnetic spectrum attenuation), the results were challenging, still the work presented here, can produce good accuracy with the datasets that have prominent feature set. The proposed algorithm can produce up to 81 percent accuracy when working with such data which accommodates conspicuous feature set, although the accuracy reduced by the factor of 36 percent if the dataset is incapacitated to fabricate distinguishable features. The paper also presents and encourage the use of ViTBAT (Video Tracking and Behaviour Annotation Tool) which is an annotation tool, especially for repeatable and reliable data.