Object tracking is aimed at tracking a given target that is only specified in the first frame. Due to the rapid movement and the interference of cluttered backgrounds, object tracking is a significant challenging issue in computer vision. This research put forward an innovative feature pyramid and optical flow estimation based on the Siamese network for object tracking, which is called SiamFP. The SiamFP jointly trains the optical flow and the tracking task under the Siamese network framework. We employ the optical flow network based on the pyramid correlation mapping to evaluate the movement information of the target in two contiguous frames, to increase the accuracy of the feature representation. Simultaneously, we adopt spatial attention as well as channel attention to effectively restrain the ambient noise, stress the target area, and better extract the features of the given object, so that the tracking algorithm has a higher success rate. The proposed SiamFP obtains state-of-the-art performance on OTB50, OTB2015, and VOT2016 benchmarks while exhibiting better real-time and robustness.

Automatic detection of granary storage states is an important technology to ensure national food security. In view of distribution characteristics of bottom pressure, this study theoretically explored a relationship between detected value of bottom pressure of granaries and granary storage states through the technologies, such as integrated learning and nonlinear regression. On this basis, this research built layout models of single-ring pressure sensors on the bottom of granaries and proposed a detection method for granary storage states based on domain knowledge of statistics of bottom pressure in granaries. The experiment demonstrates that the detection method for granary storage states based on a support vector machine (SVM) and domain knowledge shows high detection accuracy and low requirements for sensor performance and low detection cost. Therefore, it can meet the needs of remote online detection of granary storage states usually used.