Abstract
Target tracking in agricultural picking robots has a huge impact in the field of computer vision, and high-quality picking recognition and tracking algorithms are the basis for solving many problems such as inaccurate localization in agricultural picking. The problem of target recognition and tracking research is to identify and track the trajectories of different targets in video sequences. This paper mainly summarizes and analyzes the latest proposed algorithmic models about deep learning on target recognition and tracking and its application in agricultural picking robots. By analyzing various problems of current target recognition, the improvement of algorithms for different problems is summarized, and finally some possible future research directions are also proposed.
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