A Change-Aware Approach for Relative Motion Segmentation

Analysis on changes of image features is an effective means to leverage multi-dimensional information in motion segmentation. Methods without considering temporal perspective are agnostic of motion state, and inherently unsuitable for motion detection. However, the effect of existing spatio-temporal approaches is lagging behind that of spatial methods by a margin. To make better use of temporal information, this paper tackles the task of moving object segmentation by constructing a Change-aware Siamese neural network(ChaSiam) to detect relative foreground and changes. Further, a reference frame update strategy is attached to our network for overcoming the weakness of spatio-temporal approaches in cases with camera ego-motion. Extensive experiments show that our proposed model outperforms previous state-of-the-art spatio-temporal methods on Change Detection dataset, and compared with spatial methods our model has similar performance with better generalization.