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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.