DeepSORT with siamese convolution autoencoder embedded for honey peach young fruit multiple object tracking

Abstract

The young fruit bagging robot usually preferentially selects the object with largest prediction box size for bagging. However, due to the uncertainty in the output of the traditional models, the expected endpoint of mechanical motion may change constantly, which affects the bagging efficiency. This paper proposes a novel tracking algorithm for honey peach young fruit targets. Firstly, according to the proportion of young fruit prediction box in the original image, a staged data preprocessing strategy is designed. Then, the siamese convolution autoencoder (SCAE) is developed by combining autoencoding mechanism with siamese training mode, and the SCAE is trained in two-stages to ensure the discriminability of the extracted appearance features. Finally, the SCAE is inserted into the DeepSORT architecture to generate the DeepSORT-SCAE object tracking algorithm, which can measure the spatially continuous trajectory of multiple young fruit targets within video streams. The effectiveness of the developed method is verified by experiments.