In the field of actual Video Object Segmentation (VOS), traditional techniques have poor adaptability and insufficient segmentation results. Therefore, based on existing problems, an Unsupervised Video Object Segmentation (UVOS) technique based on convolutional networks is proposed. Firstly, the method of decomposing expressions is used to handle the spatiotemporal relationship between the reference frame and the target frame, and video object reconstruction is achieved through similarity calculation. For target segmentation in motion scenes, a Single Linear Bottleneck Operator (SLBO) is introduced for feature extraction, and pooling compensation is used to optimize feature information loss. For general scene segmentation, a spatiotemporal similarity segmentation technique is introduced to achieve target video segmentation for complex scenes. In the foreground segmentation test of sports scenes, the Change Detection Benchmark Dataset 2014 (CDNet.20I4SM) dataset was selected to test the model's loss performance in different scenarios. In adverse weather scenario training, the proposed model tends to converge after 40 iterations, with a loss value of 0.276, which is superior to the Foreground image Segmentation (FgSegNet_), the Convolutional Networks for Biomedical Image Segmentation (MU Net), Cascade Convolutional Neural Network (Cascade CNN) models; In the accuracy test, the proposed FS-LBPC model tended to converge after 50 iterations, with a precision P-value of 0.963. It performed the best among the four segmentation models the FgSegNet_, MU Net, Cascade CNN, and a real-time Foreground Segmentation network based on single Linear Bottleneck and Pooling Compensation (FS-LBPC). Usually, the Densely Annotated VIdeo Segmentation (DAVIS16) dataset is selected for video scene segmentation, which has the best segmentation performance in horse racing and animal flight scenes, with segmentation accuracy of 0.976 and 0.965, respectively. In summary, the VOS technology has excellent application effects in practical scenarios, providing important technical references for the improvement of image and video processing and segmentation technology.

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