End to End Background Subtraction by Deep Convolutional Neural Network In this work, we use different methods from predecessors to perform background deductions, and our most important part still uses the network framework of CNN (deep convolution neural network). The advantage of this method is obvious. We can save the process of adjusting parameters and feature engineering. Because in the process of training a single video, it can learn structural network parameters from data. At the same time, we use the SUBSENSE structure to train data. It is a background structure that can monitor the moving objects in the video. It can better detect the foreground objects camouflaged and minimize the influence of unrelated factors such as illumination. Second, we use the long and short memory network (LSTM) to predict the significance of video, which can effectively avoid a limited number of training video over fitting, using our data through training successfully to learn the spatial and temporal significance of the estimation. Through our method, the final saliency prediction has achieved good results.

See More, Know More: Unsupervised Video Object Segmentation with CoAttention Siamese Networks. We introduce a novel network, called COattention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video frames and incorporate a global coattention mechanism to improve further the stateoftheart deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in shortterm temporal segments. The coattention layers in our network provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending coattention responses into a joint feature space. We train COSNet with pairs of video frames, which naturally augments training data and allows increased learning capacity. During the segmentation stage, the coattention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. We propose a unified and endtoend trainable framework where different coattention variants can be derived for mining the rich context within videos. Our extensive experiments over three large benchmarks manifest that COSNet outperforms the current alternatives by a large margin.