Abstract
Influenced by the force of the wind and agricultural operations, fruits often undergo oscillation, which makes it difficult to automatically monitor their growing status. It is very important to realize dynamic tracking of these oscillating fruits in order to improve automatic monitoring systems and the efficiency of picking robots. In order to investigate the accuracy of the tracking of oscillating fruits, three classic tracking algorithms were adopted and compared: the kernelized correlation filter algorithm (KCF), the compressive tracking algorithm (CT), and the multi-task tracking algorithm (MTT). The effectiveness of these algorithms was verified by testing six video sequences acquired in different environments, and three indices (the average central error, frame loss rate, and time efficiency) were used to verify their performance. The results showed that the KCF algorithm was most appropriate for the tracking of oscillating fruit objects, as it has a lower centering error and a much higher frame rate.
Highlights
Under natural conditions, fruits are prone to undergoing irregular motion under the influence of the wind and disturbances caused by pruning, grafting and other agricultural operations
QUANTITATIVE RESULTS AND DISCUSSION The velocity curve for the oscillation of the fruit is shown in Figure. 1, where the abscissa is the number of frames, and the ordinate is the velocity/pixel
The red line represents the true velocity of the motion of the oscillating fruit; the purple line indicates the velocity curve obtained using the kernelized correlation filter algorithm (KCF) algorithm; the green line indicates the velocity curve obtained using the compressive tracking algorithm (CT) algorithm; and the blue line indicates the velocity curve using the multi-task tracking algorithm (MTT) algorithm
Summary
Fruits are prone to undergoing irregular motion under the influence of the wind and disturbances caused by pruning, grafting and other agricultural operations. This affects the accuracy and efficiency of the automatic monitoring of their growth and robot picking operations. It is very important to improve the efficiency of fruit growth monitoring systems and intelligent picking systems. Fast recognition and accurate positioning are two problems that need to be solved for apple harvesting robots and growth status monitoring systems [1]. Gao et al [2] proposed a fruit recognition and location algorithm, which based on the improved connected component labeling algorithm and the shape feature value circularity. Zhao et al [3] developed a robotic device for harvesting apples, which consisted of a
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