Surveillance video motion segmentation based on the progressive spatio‐temporal tunnel flow model

Motion segmentation is the first and important step of surveillance video summarisation, and traditional motion segmentation methods usually process all video data, which seriously affects the real‐time performance of video synopsis. To address this issue, a novel method of surveillance video motion segmentation based on the progressive spatio‐temporal tunnel (STT) flow model is proposed in this letter. Unlike traditional video segmentation methods, the proposed one only analyses the pixels on the circular sampling lines of video frames. Initially, the circular progressive STT is established by sampling pixels progressively. Subsequently, the progressive STT is expanded to form an STT expansion diagram and the STT expansion diagram is modelled as progressive STT flow. Finally, surveillance video motion fragments are segmented according to the progressive STT flow model. Experimental results demonstrate that the proposed method outperforms the existing state‐of‐the‐art methods in terms of time consumption. This is accomplished with a comparable motion segmentation precision.