Video change detection, which plays an important role in computer vision, is far from being well resolved due to the complexity of diverse scenes in real world. Most of the current methods are designed based on hand-crafted features and perform well in some certain scenes but may fail on others. This paper puts up forward a deep learning based method to automatically fuse multiple basic detections into an optimal
one. Specifically, a convolutional fusion neural network is designed to obtain an adaptive fusion strategy based on features extracted from video content. Limited by the amount of available labeled dataset for change detection, this paper leverages an extractor that well trained on external dataset to improve generalization. Experiments show that the proposed method generates state-of-the-art result compared with nine recent outstanding algorithms and it performs well for diverse scenarios such as dynamic background, camera jitter and night videos.