A sparse sample collection and representation method using re-weighting and dynamically updating OMP for fish tracking

Abstract

Tracking fish in their natural environment is an important aspect of marine ecosystem research. However, real-world fish tracking is challenging due to unconstrained environments and complex scenarios. The purpose of this study is to develop a sparse sample collection and representation method (SSCR) based on the compressive sensing concept for fish tracking. The SSCR consists of sample collection and sparse sample representation procedures. The sample collection procedure obtains sets of positive, negative, and predictive samples by using the proposed speed-up background modeling method (SuBM). The SuBM adopts nonparametric histogram concept for each pixel to build a background model, and efficiently accelerates the tracking speed. In addition, the sparse sample representation procedure represents each predictive sample as a sparse linear combination of all positive and negative samples. The weights of the predictive samples are computed using our proposed re-weighting and dynamically updating orthogonal matching pursuit method (RwDuOMP). The RwDuOMP, which includes three concepts (picking extra samples, re-weighting the picked samples, and dynamically updating negative samples), efficiently improves the performance of sparse signal reconstruction. The predictive sample with the maximum weight is regarded as the target object tracking result. We evaluate the SSCR method using several complicated real-world underwater sequences. Furthermore, we compare the SuBM with the Gaussian Mixture Model, and also compare the RwDuOMP method with the orthogonal matching pursuit (OMP), regularized OMP, and compressive sampling matching pursuit methods. Experimental results indicate that our proposed method achieves efficiently higher tracking results than other methods, and accelerates fish tracking.