We focus on designing GCN learning algorithm to deal with the point cloud tasks (e.g. In recent years, graph convolutional network (GCN) has been designed for dealing with graph structure data and it is powerful on some tasks besides point cloud. Point cloud is an important field in computer vision and many applications (e.g. The greater impact depends on the final fc layer i.e. In the experiments, we found that the generation of the frame drop phenomenon does not mainly originate from the backbone, the existence of the anchor and the detection head process. Our work is to analyze the similarities and differences between several adjacent frames of images and feature maps under different scenes, models, convolution depths, global and local conditions and to explore a more adequate method of inter-frame analysis and try to find out what caused the frame drop. Generally speaking, finding out why video frames are dropping will help us come up with a new solution. Despite of substantial work in this space, accuracy of classifiers on adjacent video frames remains much lower than that on normal inputs. The existing video-based object detection methods extensively apply large networks to every frame in the video to localize and classify targets, which suffers from a high computational cost and hardly meet the low-latency requirement in realistic applications. The problem of detection consistency in video object detection is a key challenge. Solutions to Overcome the Frame Dropping in Detection Networks
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