Adaptive Spatio-Temporal Tube for Fast Motion Segments Extraction of Videos

Existing motion segments extraction methods suffer from the problem of high computation complexity. To address this issue, we propose a method called adaptive spatio-temporal tube for fast motion segments extraction of videos. Firstly, initial spatio-temporal flow of sub-videos divided from input video is computed by adopting a novel Area-adjusted Spatio-Temporal Tunnel (A-STT) to screen preliminarily moiton segments. Secondly, the Sampling-line Adjustment Mechanism (SAM) is presented to avoid processing the entire amount of video spatial data and reduce computational complexity. The SAM is created by analyzing object consistency to produce a Sampling-line Adjustment Factor (SAF) which is used to dynamically obtain the sampling-line of various sub-videos. Finally, the adaptive spatio-temporal tubes are generated by integrating the initial spatio-temporal flow and SAF, which ensures the robustness of the proposed method. The proposed method is experimented on the public datasets VISOR, CAVIR and self-collected dataset. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both computing speed and accuracy.