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

TL;DR

This paper introduces a real-time optical flow based motion detection method for non-stationary scenes that is efficient, adaptable, and outperforms existing methods without requiring model training or updates.

Contribution

The paper proposes a novel optical flow framework with a dual judgment mechanism for robust, real-time motion detection in dynamic scenes without model training.

Findings

Outperforms state-of-the-art real-time methods

Effective in various scene conditions

No need for model training or updating

Abstract

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 real-time methods, indicating the advantages of our optical flow based method.