LAUSR

Change detection is widely used in city construction, remote image detection, autonomous driving and so on. The most important challenge in change detection is how to define the real change, that is to say, how to detect the real changes while ignoring the unexpected changes? such as varying illumination, shadows, seasonal scene changes. Motivated by the key issue, in this paper, we propose a novel two-classified method that utilizes the depth information to assist the semantic information for better detecting changes. A gradual modification strategy is also designed to combine the high-level semantics and the low-level edge-sensitive features to achieve better depth estimation. The proposed framework was evaluated by using the VL-CMU-CD streetscape change detection dataset. Both quantitative and qualitative experiments have been implemented for evaluating the performance of the framework under different light and seasons. Experimental results show that the proposed method outperforms most of the current state-of-the-art results.