An improved tracking algorithm of floc based on compressed sensing and particle filter

In order to solve the problem of tracking flocs during complex flocculating process, we propose an improved algorithm combining particle filter (PF) with compressed sensing (CS). The feature of flocs image is extracted via CS theory, which is used to detect the single-frame image and get the detection value. Simultaneously, the optimal estimation of particle in the space model of non-linear and non-Gaussian state is obtained by PF. Then, we correlate the optimal estimate with the detected value to determine the trajectory of each particle and to achieve flock tracking. Experimental results demonstrate that this improved algorithm realizes the real-time tracking of flocs and calculation of sedimentation velocity. In addition, it eliminates the shortcomings of heavy computation and low efficiency in the process of extracting image features , and thus guarantees the accuracy and efficiency of tracking flocs.