Research On Algorithms Of Video Object Segmentation Based On Dual Stream Network In Complex Scenes

With the rapid development of information technology,the carrier of information has also been upgraded all the time,while various emerging fields are rapidly developing.Such as autopilot,artificial intelligence and so on.Vision plays an increasingly important role in human-computer interaction,medical care,and transportation.Research on machine vision is also getting deeper and deeper.As the main carrier of information in machine vision,video contains rich information.The task of video target segmentation is to extract helpful information in a large amount of redundant information,in this way the machine can understand and distinguish the world.The existing unsupervised video target segmentation algorithms often perform poorly under complex and diverse scenes,and are easily influenced by occlusion,lighting changes,blurred pictures,drastic changes in the picture,etc.,which greatly attenuates the robustness and accuracy of automatic video target segmentation algorithms in real-world scenarios.In order to solve these problems,the paper proposes an unsupervised video target segmentation algorithm based on spatio-temporal dual-stream multi-frame feature fusion.The specific work and article innovation are as follows:1 A dual-stream full-convolutional network for video target segmentation based on existing FCN networks is designed for similar target interference and illumination change scenarios with the adding of Multi-scale integration and hole convolution.According to the actual segmentation results of the space-time network,innovative fusion strategies based on appearance models were proposed and CRF was used to refine the results.The network makes full use of the space-based appearance model and the motion model based on the time domain,and integrates the continuity characteristics of the video frames.The experimental results show that it can well deal with the video target segmentation tasks in the general scene,showing a good Lu Greatness and accuracy.2 For more complex scenes such as continuous occlusion and blurring,the idea of multi-frame feature fusion was innovatively proposed and a concept of optical flow bidirectional transfer error was defined to calculate the weights.In this way,the aggregation of multi-frame information is integrated into the spatial stream network and the time stream network,respectively.An optimization scheme for dual-flow networks is proposed,and a segmentation success rate evaluation standard is introduced.The optimized network improves the robustness and accuracy of segmentation incomplex scenes on the basis of the original network.Compared with similar algorithms,it shows good anti-jamming capability and scenes better accuracy and robustness than similar algorithms in complex scenes,which greatly improved the successing rate of video target segmentation and the practicality of the video target segmentation algorithm.