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Eye tracking is currently a research hotspot in the territory of service robotics. There is an urgent need for machine vision technique in the territory of video surveillance, and biological visual object following is one of the important basic research problems. By tracking the object of interest and recording the tracking trajectory, we can extract a structure from a video. It can also analyze the abnormal behavior of groups or individuals in the video or assist the public security organs in inquiring and searching for evidence of criminal suspects, etc. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following in visual surveillance is easily affected by factors such as occlusion, rapid object movement, and appearance changes, and it is difficult to solve these problems effectively with single-layer features. This paper adopts a visual object following algorithm based on visual information features and few-shot learning, which effectively improves the accuracy and robustness of tracking.

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Many applications show that semisupervised community detection is one of the important topics and has attracted considerable attention in the study of complex network. In this paper, based on notion of voltage drops and discrete potential theory, a simple and fast semisupervised community detection algorithm is proposed. The label propagation through discrete potential transmission is accomplished by using voltage drops. The complexity of the proposal is (| | + | |) for the sparse network with | | vertices and | | edges. The obtained voltage value of a vertex can be reflected clearly in the relationship between the vertex and community. The experimental results on four real networks and three benchmarks indicate that the proposed algorithm is effective and flexible. Furthermore, this algorithm is easily applied to graph-based machine learning methods.

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