An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes

By Junjie Huang, Wei Zou, Zheng Zhu, Jiagang Zhu. 2018

28 citations recorded by the source corpus.

Real-time motion detection in non-stationary scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource. These challenges degrade the performance of the existing methods in practical applications. In this paper, an optical flow based framework is proposed to address this problem. By applying a novel strategy to utilize optical flow, we enable our method being free of model constructing, training or updating and can be performed efficiently. Besides, a dual judgment mechanism with adaptive intervals and adaptive thresholds is designed to heighten the system’s adaptation to different situations. In experiment part, we quantitatively and qualitatively validate the effectiveness and feasibility of our method with videos in various scene conditions. The experimental results show that our method adapts itself to different situations and outperforms the state-of-the-art realtime methods, indicating the advantages of our optical flow based method.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, background): “This has been explored in [46, 54, 45, 21] for narrow angle cameras.”