Automatic tracking of multi-fruit targets with oscillation under natural growth conditions

Abstract

It is important for intelligent orchards to be able to achieve automatic monitoring of fruit growth information within a natural growing environment. The issue of how to track green and oscillating fruits under the influence of wind and farming operations is a key aspect of monitoring of the growth state of the fruit. In order to realize the accurate tracking of green fruit targets, a new method based on target tracking is proposed. First, an optical flow method is applied to realize the automatic detection of green fruit targets, and this lays the foundation for the accurate and automatic tracking of these targets. Then, Kalman and kernelized correlation filter (KCF) algorithms are applied to achieve multi-target tracking and prediction. In order to verify the performance of these different algorithms on various types of green fruit targets, experiments were carried out based on nine video sequences. The experimental results for the tracking of single, double and triple green fruit targets show that the average tracking success rates of the Kalman algorithm are 88.15%, 82.30% and 53.10%, respectively, and those of the KCF algorithm are 94.07%, 87.35% and 61.46%, respectively, meaning that the average tracking results from KCF are 5.92%, 5.05% and 8.36% higher than those from the Kalman algorithm. The time consumed is also reduced by 35.40%, 36.27% and 40.86%, respectively. The results show that it is feasible to apply the KCF algorithm to the tracking of green fruit targets.